Methods for Top-Down Multiplexed Mass Spectral Analysis of Mixtures of Proteins or Polypeptides

ABSTRACT

Applications of ion-ion reaction chemistry are disclosed in which proton transfer reactions (PTR) and real-time data analysis methods are used to (1) simplify complex mixture analysis of samples introduced into a mass spectrometer, and (2) improve resolution and sensitivity for the analysis of large proteins in excess of 50 kDa by removing charge and reducing the collisional cross section.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of, and claims, under 35 U.S.C. § 120,the benefit of the filing date of commonly-assigned and co-pending U.S.application Ser. No. 15/406,626, now U.S. Pat. No. ______, which wasfiled on Jan. 13, 2017 and which claims, under 35 U.S.C. § 119(e),priority to and the benefit of the filing date of commonly-assigned U.S.Provisional Application No. 62/278,935, filed on Jan. 14, 2016, thedisclosures of which are hereby incorporated by reference in theirentirety. The subject matter of this application is also related tocommonly-assigned and co-pending U.S. application Ser. No. 15/830,439,which was filed on Dec. 4, 2017 and which is titled “Methods for MassSpectrometry of Mixtures of Protein or Polypeptides Using ProtonTransfer Reaction” and to commonly-assigned and co-pending U.S.application Ser. No. 15/067,727, now U.S. Pat. No. ______, which wasfiled on Mar. 11, 2016, the disclosures of which are hereby incorporatedby reference herein in their entirety.

TECHNICAL FIELD

The present invention relates to mass spectrometry and, moreparticularly, relates to methods for mass spectrometric analysis ofcomplex mixtures of proteins or polypeptides by methods that includesubjecting ionized samples to proton transfer reactions to separateionized proteins and polypeptides from other molecules and performingmathematical deconvolution analysis of resulting reaction products tosimultaneously characterize a plurality of proteins and/or polypeptidesin the mixture.

BACKGROUND ART

The study of proteins in living cells and in tissues (proteomics) is anactive area of clinical and basic scientific research because metaboliccontrol in cells and tissues is exercised at the protein level. Forexample, comparison of the levels of protein expression between healthyand diseased tissues, or between pathogenic and nonpathogenic microbialstrains, can speed the discovery and development of new drug compoundsor agricultural products. Further, analysis of the protein expressionpattern in diseased tissues or in tissues excised from organismsundergoing treatment can also serve as diagnostics of disease states orthe efficacy of treatment strategies, as well as provide prognosticinformation regarding suitable treatment modalities and therapeuticoptions for individual patients. Still further, identification of setsof proteins in samples dervived from microorganisms (e.g., bacteria) canprovide a means to identify the species and/or strain of microorganismas well as, with regard to bacteria, identify possible drug resistanceproperties of such species or strains.

One important aspect of proteomics is the identification of proteinswith altered expression levels. Differences in protein and metabolitelevels over time or among populations can be associated with diseasedstates, drug treatments, or changes in metabolism. Identified molecularspecies may serve as biological markers for the disease or condition inquestion, allowing for new methods of diagnosis and treatment to bedeveloped. Conventionally, because of the large number of proteins thatare generally present in any sample extracted from natural tissue orcells, the proteins must first be separated into individual componentsby gel or capillary electrophoresis or affinity techniques, before theindividual proteins levels can be assessed and compared to a database orbetween samples.

Because it can provide detailed structural information, massspectrometry (MS) is currently considered to be a valuable analyticaltool for biochemical mixture analysis and protein identification.Conventional methods of protein analysis therefore often combinetwo-dimensional (2D) gel electrophoresis, for separation andquantification, with mass spectrometric identification of proteins.Also, capillary liquid chromatography as well as various other“front-end” separation techniques have been combined with electrosprayionization tandem mass spectrometry for large-scale proteinidentification without gel electrophoresis. Using mass spectrometry,qualitative differences between mass spectra can be identified, andproteins corresponding to peaks occurring in only some of the spectraserve as candidate biological markers.

In recent years, mass spectrometry has also gained popularity as a toolfor identifying microorganisms due to its increased accuracy andshortened time-to-result when compared to traditional methods foridentifying microorganisms. To date, the most common mass spectrometrymethod used for microbial identification is matrix-assisted laserdesorption ionization time-of-flight (MALDI-TOF) mass spectrometry. InMALDI-TOF, cells of an unknown microorganism are mixed with a suitableultraviolet light absorbing matrix solution and are allowed to dry on asample plate. Alternatively, an extract of microbial cells is usedinstead of the intact cells. After transfer to the ion source of a massspectrometer, a laser beam is directed to the sample for desorption andionization of the proteins and time-dependent mass spectral data iscollected.

The mass spectrum of a microorganism produced by MALDI-TOF methodsreveals a number of peaks from intact peptides, proteins, proteinfragments, and other molecules that constitute the microorganism's“fingerprint”. This method relies on the pattern matching of the peakprofiles in the mass spectrum of an unknown microorganism to a referencedatabase comprising a collection of mass spectra for knownmicroorganisms obtained using essentially the same experimentalconditions. The better the match between the spectrum of the isolatedmicroorganism and a spectrum in the reference database, the higher theconfidence level in identification of the organism at the genus,species, or in some cases, subspecies level. Because the method reliesupon matching the patterns of peaks in MALDI-TOF mass spectra, there isno requirement to identify or otherwise characterize the proteinsrepresented in the spectrum of the unknown microorganism in order toidentify it.

Although MALDI-TOF methods are rapid and cost effective, they havelimitations that restrict the range of applications to pathogencharacterization and identification including but not limited tovirulence detection and quantitation, resistance marker determination,strain matching, and antibiotic susceptibility testing to name a few.The information content within a MALDI mass spectrum reflects the mostabundant and ionizable proteins which are generally limited to ribosomalproteins at the experimental conditions used. Because ribosomal proteinsare highly conserved among prokaryotes, differentiation of closelyrelated microorganisms by MALDI-TOF is limited. In this case many of theribosomal proteins across closely related species contain either thesame or slightly different amino acid sequences (i.e. single amino acidsubstitutions) that cannot be effectively differentiated with lowresolution mass spectrometers. Moreover, determination of strain and/orserovar type, antibiotic resistance, antibiotic susceptibility,virulence or other important characteristics relies upon the detectionof protein markers other than ribosomal proteins which further limitsthe application of MALDI-TOF for microbial analysis. Laboratories usingMALDI-TOF for identification of microorganisms must use other methods tofurther characterize the identified microbes. In addition, the MALDI-TOFmethod's reliance upon matching spectral patterns requires a pureculture for high quality results and thus is not generally suitable fordirect testing, mixed cultures, blood culture, or other complex samplescontaining different microorganisms.

Several other mass spectrometry methods for detection of microorganismshave been used. For example, mass spectrometry-based protein sequencingmethods have been described wherein liquid chromatography is coupled totandem mass spectrometry (LC-MS/MS) and sequence information is obtainedfrom enzymatic digests of proteins derived from the microbial sample.This approach, termed “bottom-up” proteomics, is a widely practicedmethod for protein identification. The method can provide identificationto the subspecies or strain level as chromatographic separation allowsthe detection of additional proteins other than just ribosomal proteins,including those useful for characterization of antibiotic resistancemarkers and virulence factors.

In contrast to “bottom-up” proteomics, “top-down” proteomics refers tomethods of analysis in which protein samples are introduced intact intoa mass spectrometer, without enzymatic, chemical or other means ofdigestion. Top-down analysis enables the study of the intact protein,allowing identification, primary structure determination andlocalization of post-translational modifications (PTMs) directly at theprotein level. Top-down proteomic analysis typically consists ofintroducing an intact protein into the ionization source of a massspectrometer, fragmenting the protein ions and measuring themass-to-charge ratios and abundances of the various fragmentsso-generated. The resulting fragmentation is many times more complexthan a peptide fragmentation, which may, in the absence of the methodstaught herein, necessitate the use of a mass spectrometer with very highmass accuracy and resolution capability in order to interpret thefragmentation pattern with acceptable certainty. The interpretationgenerally includes comparing the observed fragmentation pattern toeither a protein sequence database that includes compiled experimentalfragmentation results generated from known samples or, alternatively, totheoretically predicted fragmentation patterns. For example, Liu et al.(“Top-Down Protein Identification/Characterization of a Priori UnknownProteins via Ion Trap Collision-Induced Dissociation and Ion/IonReactions in a Quadrupole/Time-of-Flight Tandem Mass Spectrometer”,Anal. Chem. 2009, 81, 1433-1441) have described top-down proteinidentification and characterization of both modified and unmodifiedunknown proteins with masses up to ≈28 kDa.

An advantage of a top-down analysis over a bottom-up analysis is that aprotein may be identified directly, rather than inferred as is the casewith peptides in a bottom-up analysis. Another advantage is thatalternative forms of a protein, e.g. post-translational modificationsand splice variants, may be identified. However, top-down analysis has adisadvantage when compared to a bottom-up analysis in that many proteinscan be difficult to isolate and purify. Thus, each protein in anincompletely separated mixture can yield, upon mass spectrometricanalysis, multiple ion species, each species corresponding to adifferent respective degree of protonation and a different respectivecharge state, and each such ion species can give rise to multipleisotopic variants. A single MS spectrum measured in a top-down analysiscan easily contain hundreds to even thousands of peaks which belong todifferent analytes—all interwoven over a given m/z range in which theion signals of very different intensities overlap and suppress oneother.

Because mass spectra of biological samples, as obtained in top-downanalyses, are generally very complex, improved methods are required forinterpreting the mass spectra. The resulting computational challengethat such methods must overcome is to trace each peak back to a certainanalyte(s) and, once this is done for one or several analytes, todetermine the molecular weights of analyte(s) in a process which is bestdescribed as mathematical decomposition (also referred to, in the art,as mathematical deconvolution). A still further challenge associatedwith the use of mass spectral analyses of proteins and polypeptides in aclinical setting is to derive such information in the shortest timeperiod possible, often termed as analysis in “real time”. Obviously, thecomputations are much more challenging in real time during an automatictop-down data dependent analysis since this should occur very fast,especially when chromatographic separation is involved. To succeed, oneneeds to provide both: (i) an optimized real time computational strategyas well as (ii) a mass spectral data acquisition strategy thatanticipates multiple mass spectral lines for each ion species and thatanticipates efficient isolation of analyte compounds of interest from apotential multitude of contaminant compounds.

The existing data dependent and dynamic exclusion workflow techniquesand corresponding algorithms were developed for small molecules, smallpeptides and other analytes which acquire a limited number of charges(for example, 1-3 charges) in the electrospray ionization process. Whenapplied to higher-molecular-weight biopolymer analytes (most commonly,intact proteins during the course of top-down proteomics studies) theseconventional methodologies significantly under-perform due to acombination of different electrospray behavior and computationallimitations. More specifically: (1) intact high mass analytes ingeneral, and proteins in particular, develop many more charge states (upto 50 charges or more per molecule, e.g., FIG. 12C) than do smallmolecules during the electrospray ionization process because of agreater number of charge acquiring sites which results in much morecomplex MS spectra; (2) in complex mixtures such as cell lysates ortheir fractions, there is a wide distribution of molecular weights andcopy numbers which results in a very complex overlap of charge statedistribution patterns of varying intensities; (3) the variability inphysiochemical properties of the high-mass analytes of the same ordifferent chemical nature produces significant variability ofchromatographic peak shapes and analyte retention on the column; (4) ifthe mass spectra are acquired on a mass spectrometer with high resolvingpower such as an Orbitrap™ mass analyzer (a type of electrostatic trapmass analyzer) or a time-of-flight (TOF) mass analyzer, correspondingpeaks further resolve into a number of isotopes in a series of clusterswhose quality is often far from a theoretical binomial distribution; (5)matrix ionization effects of a variety of different proteins can greatlyinfluence the observed intensity of multiply overlapping species so asto distort the true ratios of protein intensities found in any givenstandard or sample. Additional levels of complexity are introduced byoxidized species of the same analyte or adducts, overlaps of isotopeclusters and inability of existing software tools correctly calculatecharge state for high mass species.

In practical terms, the above considerations imply that, in the case ofintact proteins and other biopolymers, existing data dependentalgorithms are being confounded and MS/MS is being performed in aredundant fashion on a number of different charge states from the samebiopolymer. Also, when isotopic clusters do not match the traditionalbinomial distribution patterns defined by the number of carbon,hydrogen, nitrogen, oxygen, and sulfur atoms present in a givenbiopolymer, or do not meet intensity threshold or signal-to-noiserequirements, redundancy occurs from fragmenting multiple isotopes whichbelong to the same isotopic cluster. This duplication of work leads toredundancy in identification of the most abundant/ionizable proteins,while the information about other species is lost and provides verylittle opportunity for triggering an MS^(n) analysis.

With regard to efficient instrument-associated data acquisitionstrategies, it may be noted that ion-ion reactions have found greatutility in the field of biological mass spectrometry over the lastdecade, primarily with the use of electron transfer dissociation (ETD)to dissociate peptide/proteins and determine primary sequenceinformation and characterize post-translational modifications. Protontransfer, another type of ion-ion reaction, has also been usedextensively in biological applications. Experimentally, in one form ofproton transfer, multiply-positively-charged protein ions (i.e., proteincations) from a sample are allowed to react with singly-charged reagentanions so as to reduce the charge state of an individual protein cationand the number of such charge states of the protein cations. Thesereactions proceed with pseudo-first order reaction kinetics when thereagent anions are present in large excess over the protein cationpopulation. The rate of reaction is directly proportional to the squareof charge of the protein cation (or other multiply-charged cation)multiplied by the charge on the reagent anion. The same relationshipalso holds for reactions of the opposite polarity, defined here asreaction between singly-charged reagent cations and a population ofmultiply-charged anions derived from a protein sample. This produces aseries of pseudo-first order consecutive reaction curves as defined bythe starting multiply-charged protein cation population. Although thereactions are highly exothermic (in excess of 100 kcal/mol), protontransfer is an even-electron process performed in the presence of 1mtorr of background gas (i.e. helium) and thus does not fragment thestarting multiply-charged protein cation population. The collision gasserves to remove the excess energy on the microsecond time scale (10⁸collisions per second), thus preventing fragmentation of the resultingproduct ion population.

Proton transfer reactions (PTR) have been used successfully to identifyproteins in mixtures of proteins. Particularly, application of protontransfer reaction methods may be envisioned as a mixture simplificationprocess that is carried out in real-time (a few milliseconds) in a massspectrometer that separates mass spectral signatures of proteins andpolypeptides from one another as well as from generally low-chargecontaminant ions. This procedure enables isolation of the analyteproteins and polypeptide ions either as a group or as individual ionspecies and has thus been employed to determine charge state andmolecular weights of high mass proteins. PTR has also been utilized forsimplifying product ion spectra derived from the collisional-activationof multiply-charged precursor protein ions. Although PTR reduces theoverall signal derived from multiply-charged protein ions, this is morethan offset by the significant gain in signal-to-noise ratio of theresulting PTR product ions. The PTR process is 100% efficient leading toonly single series of reaction products, and no side reaction productsthat require special interpretation and data analysis.

Various aspects of the application of PTR to the analysis of peptides,polypeptides and proteins have been described in the followingdocuments: U.S. Pat. No. 7,749,769 B2 in the names of inventors Hunt etal., U.S. Patent Pre-Grant Publication No. 2012/0156707 A1 in the namesof inventors Hartmer et al., U.S. Pre-Grant Publication No. 2012/0205531A1 in the name of inventor Zabrouskov; McLuckey et al., “Ion/IonProton-Transfer Kinetics: Implications for Analysis of Ions Derived fromElectrospray of Protein Mixtures”, Anal. Chem. 1998, 70, 1198-1202;Stephenson et al., “Ion-ion Proton Transfer Reactions of Bio-ionsInvolving Noncovalent Interactions: Holomyoglobin”, J. Am. Soc. MassSpectrom. 1998, 8, 637-644; Stephenson et al., “Ion/Ion Reactions in theGas Phase: Proton Transfer Reactions Involving Multiply-ChargedProteins”, J. Am. Chem. Soc. 1996, 118, 7390-7397; McLuckey et al.,“Ion/Molecule Reactions for Improved Effective Mass Resolution inElectrospray Mass Spectrometry”, Anal. Chem. 1995, 67, 2493-2497;Stephenson et al., “Ion/Ion Proton Transfer Reactions for ProteinMixture Analysis”, Anal. Chem. 1996, 68, 4026-4032; Stephenson et al.,“Ion/Ion Reactions for Oligopeptide Mixture Analysis: Application toMixtures Comprised of 0.5-100 kDa Components”, J. Am. Soc. MassSpectrom. 1998, 9, 585-596; Stephenson et al., “Charge Manipulation forImproved Mass Determination of High-mass Species and Mixture Componentsby Electrospray Mass Spectrometry”, J. Mass Spectrom. 1998, 33, 664-672;Stephenson et al., “Simplification of Product Ion Spectra Derived fromMultiply Charged Parent Ions via Ion/Ion Chemistry”, Anal. Chem., 1998,70, 3533-3544 and Scalf et al., “Charge Reduction Electrospray MassSpectrometry”, Anal. Chem. 2000, 72, 52-60. Various aspects of generalion/ion chemistry have been described in McLuckey et al., “Ion/IonChemistry of High-Mass Multiply Charged Ions”, Mass Spectrom. Rev. 1998,17, 369-407 and U.S. Pat. No. 7,550,718 B2 in the names of inventorsMcLuckey et al. Apparatus for performing PTR and for reducing ion chargestates in mass spectrometers have been described in U.S. Pre-GrantPublication No. 2011/0114835 A1 in the names of inventors Chen et al.,U.S. Pre-Grant Publication No. 2011/0189788 A1 in the names of inventorsBrown et al., U.S. Pat. No. 8,283,626 B2 in the names of inventors Brownet al. and U.S. Pat. No. 7,518,108 B2 in the names of inventors Frey etal. Adaptation of PTR charge reduction techniques to detection andidentification of organisms has been described by McLuckey et al.(“Electrospray/Ion Trap Mass Spectrometry for the Detection andIdentification of Organisms”, Proc. First Joint Services Workshop onBiological Mass Spectrometry, Baltimore, Md., 28-30 Jul. 1997, 127-132).

The product ions produced by the PTR process can be accumulated into oneor into several charge states by the use of a technique known as “ionparking”. Ion parking uses supplementary AC voltages to consolidate thePTR product ions formed from the original variously protonated ions ofany given protein molecule into a particular charge state or states atparticular mass-to-charge (m/z) values during the reaction period. Thistechnique can be used to concentrate the product ion signal into asingle or limited number of charge states (and, consequently, into asingle or a few respective m/z values) for higher sensitivity detectionor further manipulation using collisional-activation, ETD, or other ionmanipulation techniques. Various aspects of ion parking have beendescribed in U.S. Pat. No. 7,064,317 B2 in the name of inventorMcLuckey; U.S. Pat. No. 7,355,169 B2 in the name of inventor McLuckey;U.S. Pat. No. 8,334,503 B2 in the name of inventor McLuckey; U.S. Pat.No. 8,440,962 B2 in the name of inventor Le Blanc; and in the followingdocuments: McLuckey et al., “Ion Parking during Ion/Ion Reactions inElectrodynamic Ion Traps”, Anal. Chem. 2002, 74, 336-346; Reid et al.,“Gas-Phase Concentration, Purification, and Identification of WholeProteins from Complex Mixtures”, J. Am. Chem. Soc. 2002, 124, 7353-7362;He et al., “Dissociation of Multiple Protein Ion Charge States Followinga Single Gas-Phase Purification and Concentration Procedure”, Anal.Chem. 2002, 74, 4653-4661; Xia et al., “Mutual Storage Mode Ion/IonReactions in a Hybrid Linear Ion Trap”, J. Am. Soc. Mass. Spectrom.2005, 16, 71-81; Chrisman et al., “Parallel Ion Parking: ImprovingConversion of Parents to First-Generation Products in Electron TransferDissociation”, Anal. Chem. 2005, 77(10), 3411-3414 and Chrisman et al.,“Parallel Ion Parking of Protein Mixtures”, Anal. Chem. 2006, 78,310-316.

As a result of the ongoing requirement in the art of mass spectralproteome analysis for analysis of complex natural samples in real-timeor near-real-time, there is thus a need for improved methods of massanalysis, both instrumental and computational, that can efficientlyseparate analytes from contaminants, differentiate signal from noise,correctly allocate related m/z values into proper isotopic clusters,correctly determine charge states and properly organize the variouscharge states into distribution envelopes. Such improvements arerequired for success in both data acquisition and, optionally,post-acquisition processing workflows. Preferably, the improvedinstrumental methods, workflows and algorithms should be able to work ina “real-time” environment such that automated data-dependent decisionsmay be made while mass spectra are being acquired and such that clinicalinterpretations may be made shortly therafter. The present disclosureaddresses these needs.

DISCLOSURE OF INVENTION

The present disclosure teaches an application of ion-ion reactionchemistry in which: (i) one or more stages of proton transfer reactions,optionally supplemented by data-dependent fragmentation, are employed tosimplify the mass spectrometric analysis of complex ion populationsderived from electrospray ionization of samples comprising mixtures ofcompounds extracted from samples of tissues, biological fluids,microorganisms or other cells; and (ii) an optimized spectraldeconvolution procedure is employed to automatically discriminatebetween mass spectral signatures, in the simplified spectra, of aplurality of biopolymer molecules in a sufficiently short time (i.e.,computation time of one second or less) such that decisions may be madein real time regarding the course of subsequent mass spectral analysissteps of the same respective sample.

In particular, the inventors have discovered that by subjecting amass-to-charge-restricted subset of such ions to PTR, the resultingpopulation of product ions comprises a much simpler population of chargestates of lower total charge values (where the words “lower” or“reduced”, in this context, refer to lower or reduced in terms ofabsolute value) which can be readily resolved and assigned to specificprotein or peptide ions. Because the PTR product ions represent asmaller subset of multiply-charged species derived from a complexmixture of charge states than the original precursor ions, mass spectralinterpretation is greatly simplified and target analysis using tandemmass spectrometry (MS/MS or MS^(n)) can be performed on a single proteinor other component(s) derived from a microbial extract.

The charge-reduced protein and peptide product ions resulting from agiven proton transfer reaction produce mass-to-charge (m/z) values thatare greater than those of the original m/z values. For a mixture ofprotein ions that have the same m/z value but differing mass and charge,the mixture can be separated on the micro- or millisecond timescale.Further, these multiply-charged protein ions of the same m/z value withdiffering mass and charge can be separated from low m/z value backgroundions derived from small molecules, lipids, solvents, or otherinterferents based on the charge squared dependence of the reaction.Multiply-charged ions are therefore separated in time from thebackground signal thus producing a separated protein mixture at highlyincreased signal-to-noise (s/n) ratio. The inventors have discoveredthat, as a result of these two factors, the spectral signatures of theprotein/peptide or any other analyte product ions may be significantlyseparated from those of most interferent ions. In addition, multiplestages of PTR reactions can be performed to separate protein mixtures onlow resolution instrumentation, such as a linear ion trap massspectrometer, in order to simplify and isolate these proteins and otheranalytes such that target analysis can be performed via MS^(n) analysis.The inventors have further discovered that the advantageous propertiesof simple PTR reactions may be even further amplified by performing “ionparking” procedures in conjunction with PTR reaction, thus enabling ananalyst to at least partially select or control the product-ion chargestate distribution that results from the PTR reaction.

PTR can also be used to improve high mass performance in massspectrometry. In mass spectrometry, an ion may be assigned either aninteger nominal mass or mass-to-charge ratio or an accurate or exactmass or mass-to-charge ratio. Accurate or exact masses or mass-to-chargeratios can be considered as comprising an integer component or value anda decimal component or value. Atomic and molecular masses are measuredin units of daltons (Da) and m/z ratio values are generally given in inunits of daltons per elementary charge, or Dale or thomson (Th). It isto be noted that, in instances of described numerical values of m/zratios in this document, such ratios are understood to be provided inunits of daltons per elementary charge, or Th. Accurate or exact (i.e.non-integer) masses or m/z ratios can be represented as an integernominal mass or mass-to-charge ratio value or component together with acorresponding decimal component. Thus, as used in this document,accurate mass determination or mass analysis can be considered ascomprising sub-integer accuracy, i.e. accuracy of ±0.5 Da or better and,preferably, 0.1 Da or better.

Alternatively, accurate or exact masses or m/z ratios may be defined interms of parts-per-million (ppm) mass accuracy. For mass spectrometricdeterminations of polypeptides and proteins, an experimental massaccuracy of 50 ppm or better, more preferably 10 ppm or better and,still more preferably 1 ppm or better, is generally required becausesuch molecules and their ions frequently have molecular or ionic weightsof at least 10,000 Da and as much as 100,000 Da. Thus, as used in thisdocument, accurate mass determination or mass analysis can alternativelybe considered as comprising an accuracy of 50 ppm or better, morepreferably 10 ppm or better and, still more preferably, 1 ppm or better.

In addition to improving the signal-to-noise ratios for this type ofanalysis, the inventors have considered that the reduction of charge onprotein ions causes these large ions to refold in the gas phase, as hasbeen described in Zhao et al., “Effects of Ion/Ion Proton TransferReactions on Conformation of Gas-Phase Cytochrome c Ions”, J. Am. Soc.Mass Spec. 2010, 21, 1208-1217. It is believed that this results in amore compact configuration which reduces the collisional cross sectionof the protein ions and, accordingly, increases their stability againstfragmentation by collision with background gas molecules present in themass analyzer chamber. The inventors have discovered that this effectcan be especially beneficial with mass analyzers that employ imagecurrent detection, such as is done in a Fourier-transform ion cyclotronresonance (FT-ICR) mass analyzer or in an Orbitrap™ mass analyzer (atype of electrostatic trap mass analyzer commercially available fromThermo Fisher Scientific of Waltham, Mass. USA). Another potentialreason for improved high mass performance is the large deposition ofenergy into a given protein ion that results from the PTR process. Theenergy deposited as a result of the PTR process exceeds 100 kcal/mol andis then effectively dampened by the presence of collision energy. Thisrapid heating process “boils off” neutral molecules that may be attachedto the protein via ion-dipole, ion-induced dipole, or dipole-induceddipole interactions. Most importantly, the reduction of charge state forhigh mass proteins may significantly improve the transfer of these ionsfrom the relatively high pressure of an ion guide, ion storage or iontrapping device where the PTR process is commonly performed, to alower-pressure region of a mass analyzer, such as an Orbitrap™ massanalyzer. The reduced charge state means that ions are transferred atless kinetic energy thus limiting ion scattering, direct fragmentation,or formation of metastable species. The inventors further consider thatthis latter property is especially significant in enabling high-accuracymass analysis of the PTR product ions in an accurate-massspectrometer—such as the Orbitrap™-type of electrostatic trap massanalyzer—that detects image currents produced by cyclic ionic motionover an extended time range.

The present teachings are especially useful for the analysis andidentification of intact proteins having molecular weight in excess of50 kDa. The inventors have discovered the surprising result that, takentogether, the various advantageous factors noted above can enableaccurate identification of multiple intact proteins or large peptidesfrom even very complex mixtures derived from natural microorganismsamples. Such identifications can enable microorganism identification tothe species, subspecies or even strain level. The target protein orpolypeptide ion single species or multiple species may be chosen so asto be indicative, based on prior knowledge or information, eitherindividually or in combination, of the presence in a sample of aspecific microorganism or cell type, or a specific strain or variant ofa microorganism or cell type, or a given virulence factor or toxin, orof the capacity of a microorganism or cell to resist an antimicrobialcompound or antibiotic drug.

The present invention, in one aspect, offers an alternative totraditional bottom-up proteomics methods, namely top-down analysis ofintact proteins derived from microbial cells via a method which isapplicable to substantially all microorganisms including Gram-positivebacteria, Gram-negative bacteria, mycobacteria, mycoplasma, yeasts,protozoans, filamentous (i.e., microscopic) fungi. The present inventionprovides identification of microorganisms at the genus, species,subspecies, strain pathovar, and serovar level even in samplescontaining mixtures of microorganisms and/or microorganisms analyzeddirectly from pure and/or mixed cultures and from direct samples (e.g.,surface swabs, bodily fluids, etc.). In addition, the approaches taughtherein can be employed for targeted detection of virulence factors,antibiotic resistance and susceptibility markers, or othercharacteristics. The top-down methods of the present teachings aresimple and quick because there is no need for chemical or enzymaticdigestion of a sample and data processing is accomplished in real time.

Methods in accordance with the present teachings may comprise at leastone or more of the following steps: microbial cell disruption,solubilization of proteins, sample clean-up (to desalt, remove insolublecomponents and debris, and/or concentrate), sample infusion or flowinjection, fast partial liquid chromatographic separation, standardchromatographic separation, isoelectric focusing, ionization of proteinsin solution, isolation of a given m/z range of the ions, causing theisolated range of ions to undergo PTR so as to form first-generation PTRproduct ions, optional isolation of an m/z range of the first-generationPTR product ions, optional mass spectrometry in MS or MS/MS mode,optionally causing the isolated range of first-generation PTR productions to undergo a second PTR reaction so as to form second-generationPTR product ions, mass spectrometry in MS or MS/MS mode, and microbialidentification via molecular weight analysis and/or protein sequenceanalysis, or using any statistical classification method. Preferably,but not necessarily, the mass spectrometry steps are performed with ahigh-resolution, high-accuracy mass spectrometer, such as a massspectrometer comprising an Orbitrap™ mass analyzer.

Because a common method using a limited set of chemical reagents isperformed, the methods of the present teachings are suitable for usewithin a completely automated system for sample preparation and massspectrometry. Ideally, these methods may be automated from samplepreparation through results reporting. Results may be automaticallytransferred to a hospital's electronic medical records system where theycan be directly linked to patient treatment strategies, insurance,billing, or used in epidemiological reporting. Such an integrated systemfacilitates epidemiological tracking of an outbreak at the hospital,local, regional, and global levels. For high throughput laboratories,multiple systems can be interfaced to a central computer whichintegrates data from the different instruments prior to reporting. Thesystem can import phenotypic susceptibility data where it can becombined with identification, virulence, antibiotic resistance andtyping information generated by the invention.

Computational methods described herein enable both effective (1)non-redundant data dependent mass spectrometry analysis employing massspectral workflow decision-making based on results of the computationsand, optionally, post-acquisition data processing for individual highmass analytes and their mixtures of different complexities. For datadependent mass spectrometry analysis, the herein-described novel “Top PUnique Analyte-Specific Clusters” workflow and associated computationreplaces the previous conventional state-of-the-art “Top P Most AbundantPrecursors” logic. Each such species-correlative envelope is a set ofrelated mass spectral lines (m/z values) which are indicated, accordingto the methods of the present teachings, to all be generated from asingle unique molecule. Each species-correlative envelope groupstogether various charge states and isotopic clusters that are indicatedto have been produced from a single molecular species. The method alsoworks with mass spectral data in which no peaks attributable to isotopicdistributions are observed (such as may be the case for low-resolutiondata) or with mass spectral data having resolved isotopic distributionsbut only one charge state per molecule. However, the species-correlativeenvelope can exclude adducts if desired, which are removed prior to dataanalysis.

Tandem mass spectrometry (or, more-generally, MS^(n) analysis) may beperformed, based on the computational results, only on selectedrepresentatives of a given species-correlative charge state distributionenvelope after which data acquisition is directed to the nextspecies-correlative charge state distribution envelope (i.e., of adifferent compound) that is determined in a preceding MS spectrum, andso on. In various embodiments, the computations are made using dataderived from one or more stages of application of proton transferreaction (PTR), as noted above, to subsets of ions derived from abiological sample comprising a complex mixture of proteins and/orpolypeptides and other organic molecules. Prior to MS^(n) analysis,computed charge state distribution patterns are filtered so as toexclude oxidized (or other specified) species of the same analyte andvarious other unwanted adducts. In this approach, the most possibleabundant information on the analytes in a sample is retrieved either ona chromatographic time scale, or in experiments in which sample isintroduced into a mass spectrometer by infusion, flow injection or bymeans of any other sample introduction device. In all cases,data-acquisition redundancy is either totally eliminated orsignificantly reduced.

The optimized “Top P Unique Analyte-Specific Clusters” computationalworkflow may include one or more of: (1) use of centroids forrepresenting peak positions; (2) use of either a binary or simplifiedintensity scale for representing peak heights; (3) correct computationalassignment of charge state to each peak (centroid) in isotopic clustersfound in a scan; (4) the use of information on charge state to assignisotopic clusters (either resolved or unresolved) to the appropriatecharge-state envelope(s); (5) optional determination of molecularweights; and (6) the control of data-dependent acquisition in a way toallow only one (or a selected number) of MS^(n) event(s) per eachindividual charge state envelope. The “Top P Unique Cluster” method canbe set up to recognize and work with either the most intense chargestate for a given biopolymer, the median charge state between thehighest charge state detected and the most intense charge stateobserved, or any other desired charge state (i.e., not just a maximumabundance or median charge state) or combination of charge states. Themethod is therefore well-suited for use with a variety of ion activationmethods used for ion fragmentation including but not limited tocollision-induced dissociation (CID) and electron-transfer dissociation(ETD), defined for a given molecular weight range, or in instances inwhich the least abundant proteins species are interrogated first.Similar methods may be employed for post-acquisition data processing, inwhich the same computation logic is applied to raw MS spectra for whichacquisition is completed prior to execution of the novel methods. Eitherreal-time or post-acquisition data processing may further includemolecular weight determination and analyte identification.

These principles of the present teachings can be applied for analytes ofvarious molecular weights and chemical nature on high resolution tandemmass spectrometry systems including but not limited to mass spectrometerinstruments that are based on or include an Orbitrap™ mass analyzer.Such instruments include Orbitrap Fusion™, Orbitrap Velos-Pro™,Q-Exactive™, and Orbitrap Elite™ as well as quadrupole time-of-flight(QTOF) mass spectrometers and Fourier transform ion cyclotron resonance(FT-ICR) mass spectrometers. Further, the same computational principlescan be applied to isotopically unresolved charge state envelopes whichcan be seen in mass spectra obtained on high resolution massspectrometry systems for comparatively very high mass analytes, or tounit resolution mass spectra obtained on mass analyzers such as linearion traps or any other Paul trap configuration. In instances, instead ofmaking charge determinations based on a distance between individuallyresolved lines of isotopic clusters, these are instead calculated usingdistances between charge states within the same charge state envelope.Again, this clustering based strategy can be applied to unit resolutiondata as well as to data generated by linear ion traps and triplequadrupole instrumentation.

When used in conjunction with chromatographic separation, the proposedcomputational workflow methods maximize information from each individualmass spectrum obtained during the course of a chromatographic run. Thenovel methods may also be employed in conjunction with mass spectralexperiments in which sample is introduced by infusion or flow injection.In most experimental situations, the novel methods significantly reducetotal analysis time. When applied to data already acquired, the novel“Top P Unique Analyte-Specific Clusters” workflow methods can maximizethe information yield from MS spectra and can calculate the molecularweights of the analytes in real time.

The novel principles, workflows and algorithms and methods described andtaught in this disclosure are applicable in all cases when severalanalytes are mass spectrometrically (MS) detectable within the same massspectrum. For example, the novel teachings may be employed in cases inwhich two or more analytes co-elute from a chromatographic column andthe co-eluting analytes are simultaneously introduced into a massspectrometer. As a second example, the novel teachings may be employedin cases in which two or more analytes are introduced into a massspectrometer using a flow injection methodology. In yet a third example,the novel teachings may be employed in cases in which two or moreanalytes are introduced into a mass spectrometer using syringe infusion.In still yet other examples, the novel teachings may be employed incases in which analytes are introduced into a mass spectrometer afterseparation by a capillary electrophoresis apparatus or a lab-on-a-chipapparatus. The novel methods may be employed in conjunction with massspectrometers employing any known ionization technique that generatesmultiply-charged ions, such as, without limitation, electrosprayionization (ESI).

Accordingly, in a first aspect, there is disclosed a method foridentifying the presence or absence of a protein/polypeptide or otherbiologically relevant compound within a liquid sample comprising amixture of compounds that includes a plurality of protein compounds or aplurality of polypeptide compounds or pluralities of both protein andpolypeptide or other compounds, wherein the method comprises: (a)introducing a portion or all of the liquid sample into an electrosprayionization source of a mass spectrometer; (b) forming positively-chargedions of the mixture of compounds of the portion of the liquid sample byelectrospray ionization, the positively-charged ions comprising aplurality of ion species; (c) isolating a first subset of the ionspecies comprising a first mass-to-charge (m/z) ratio range thatincludes an m/z ratio of a multiply-protonated molecular species of theanalyte compound; (d) generating a plurality of first-generation production species from the isolated first subset of ion species by causing theisolated first subset of ion species to be reacted, for a predeterminedtime duration, with reagent anions that, upon reaction, extract protonsfrom each of one or more ion species that comprises a protonated speciesof a protein or polypeptide compound; (e) generating a mass spectrum,using a mass analyzer, of either the first-generation product ionspecies or of second-generation product ion species generated from thefirst-generation product ion species; (f) conducting a search of themass spectrum of either the first-generation or the second-generationproduct ion species for a set of one or more m/z ratios that arediagnostic of the protein or polypeptide analyte compound; and (g)making a determination of the presence or absence of the analytecompound within the sample based on a measure of similarity between aset of m/z ratios identified in the mass spectrum and the set of one ormore diagnostic m/z ratios. The measure of similarity may comprise ametric that is calculated based on a determined percentage or proportionof the one or more diagnostic m/z ratios that are found to occur in themeasured set of identified m/z ratios. Alternatively, the presence of ananalyte compound within the sample can be determined by comparing themeasure of similarity between a set of m/z ratios identified in the massspectrum and those contained in a protein, DNA, or carbohydrate baseddatabase; and (h) using the aforementioned information as a way topositively identify any unknown microorganism using spectral libraries,sequence based searching, statistical classification methods includingbut not limited to Bayesian, logistic regression, and decision treeclassifiers. As an alternative to forming positively-charged ions instep (b), negatively-charged analyte ion species may be producedinstead. In such cases, the reagent anions are chosen so as to transferprotons to the analyte ion species, thereby reducing the absolute valuesof their negative charges.

In a second aspect, there is disclosed a method of identifying thepresence or absence of a microorganism type in a sample, comprising: (i)identifying a series of molecular weights whose simultaneous presence inthe sample is diagnostic of the presence of the microorganism type(s) inthe sample (ii) identifying a list of analyte compounds whosesimultaneous presence in the sample is diagnostic of the presence of themicroorganism type(s) in the sample, said list of analyte compoundscomprising protein compounds, polypeptide compounds or both protein andpolypeptide compounds; (iii) extracting, from the sample, a liquidsolution comprising a mixture of sample-derived proteins andpolypeptides; (iv) performing a set of analysis steps for eachrespective analyte compound in the list; and (v) identifying thepresence of the microorganism(s) type within the sample if the presenceof microorganism specific analyte compounds of the list of analytecompounds is identified within the liquid solution. The analysis stepsthat are performed for each respective analyte in the list comprise: (a)introducing a portion of the liquid solution into an electrosprayionization source of a mass spectrometer; (b) forming positively-chargedions of the mixture of compounds of the portion of the liquid solutionby electrospray ionization, the positively-charged ions comprising aplurality of ion species; (c) isolating a first subset of the ionspecies comprising a first mass-to-charge (m/z) ratio range thatincludes an m/z ratio of either a random or particular predeterminedmultiply-protonated molecular species of the respective analytecompound; (d) generating a plurality of first-generation product ionspecies from the isolated first subset of ion species by causing theisolated first subset of ion species to be reacted, for a predeterminedtime duration, with reagent anions that spontaneously extract protonsfrom each of one or more ion species that comprises a protonated speciesof a protein or polypeptide compound; (e) generating a mass spectrum,using a mass analyzer, of either the first-generation product ionspecies or of second-generation product ion species generated from thefirst-generation product ion species; (f) conducting a search of themass spectrum of either the first-generation or the second-generationproduct ion species for a set of one or more m/z ratios that arediagnostic of the respective analyte compound; and (g) identifying thepresence of the respective analyte compound within the liquid solutionbased on a measure of similarity between a set of m/z ratios identifiedin the mass spectrum and the set of one or more diagnostic m/z ratios.The measure of similarity may comprise a metric that is calculated basedon a determined percentage or proportion of the one or more diagnosticm/z ratios that are found to occur in the measured set of identified m/zratios. The diagnostic m/z ratios can be derived from a spectral libraryor sequence database. If the m/z ratio that is isolated in step (c) isof a random multiply-protonated molecular species, then the searchconducted in step (f) is a sequence-based search. Otherwise, if the m/zratio that is isolated in step (c) is of a particular predeterminedmultiply-protonated molecular species, then a spectral library search isconducted in step (f). In addition to using the aforementionedinformation as a way to positively identify an unknown microorganismusing spectral libraries or sequence based searches, statisticalclassification methods including but not limited to Bayesian, logisticregression, and decision tree classifiers can be utilized for microbialcharacterization and identification. As an alternative to formingpositively-charged ions in step (b), negatively-charged analyte ionspecies may be produced instead. In such cases, the reagent anions orcations are chosen so as to transfer protons to the analyte anionspecies, thereby reducing the absolute values of their negative charges.Control of the PTR experimental processes described herein can beperformed manually or automatically in real-time using real-timespectral deconvolution.

The term “real-time spectral deconvolution” in the above refers tospectral deconvolution of mass spectral data that is performedconcurrently with the mass spectral experiment or analytical run thatgenerates (or that has generated) that mass spectral data. For example,mass spectral data acquired by mass analysis of analytes that elute at afirst retention chromatographic retention time during a gradient elutionmay be deconvoluted, so as to identify the analytes, simultaneously withthe continued collection of additional mass spectral data of additionalanalytes that elute at a second, later retention time during the samegradient elution. Likewise, deconvolution of the additional massspectral data, so as to identify the additional analytes, may beperformed simultaneously with the continued collection of mass spectraldata of analytes that elute at a third elution time during the samegradient elution. The real-time spectral deconvolution may befacilitated by the use of a fast computer, such as a computer thatemploys parallel processing or a graphics processing unit (GPU) toperform the necessary calculations. Alternatively or additionally, thereal-time spectral deconvolution may be facilitated by the use of acomputationally efficient or optimized algorithm, such as an algorithmthat is written at least partially in assembly language or that makesextensive use of in-cache look-up-tables. Advantageously, and asprovided by the deconvolution computational methods in accordance withthe present teachings (described in the appendix), the mathematicalcomputations will not introduce any significant delays (i.e., greaterthan 1.0 seconds) into the work flow, taken with respect to the sameworkflow in the absence of the execution of the deconvolutioncomputations.

More generally, the term “real-time” may be understood as meaning, whenused in reference to an event or activity associated with a dataacquisition process, that the event or activity occurs while some aspector sub-process of that data acquisition process is ongoing. The dataacquisition process itself may include one of more the followingindividual sub-processes: sample purification (e.g., solid phaseextraction, size-exclusion chromatography); sample separation (e.g.,chromatography); sample transfer into a mass spectrometer (e.g.,infusion or inletting of eluate from a chromatograph); sample ionizationin an ion source to as to generate first-generation ions; selection andisolation of ions for further manipulation; causing fragmentation ofsample-derived ions or reaction of sample-derived ions with reagent ionsso as to generate a first-generation of product ions; optional selectionand isolation of product ions; optional further fragmentation of productions or further reaction of product ions; transfer of ions(first-generation ions or first-generation or subsequent-generationproduct ions) to a mass analyzer, detection and measurement of ionmass-to-charge ratios by a detector of the mass analyzer; and transferof data derived from the detection and measurement to a digitalprocessor for storage, mathematical analysis, etc. The events oractivities that may occur in “real-time”, so defined, may include, butare not necessarily limited to: determination or identification of thepresence of an analyte in a sample; identification or determination ofthe presence of a microorganism in a sample and providing a notificationto a user of the identification or determination of the presence of ananalyte or microorganism in a sample.

The above-described and various other features and advantages of thepresent teachings will become more fully apparent from the followingdescription and appended claims, or may be learned by the practice ofthe invention as set forth hereinafter.

BRIEF DESCRIPTION OF DRAWINGS

To further clarify the above and other advantages and features of thepresent disclosure, a more particular description of the disclosure willbe rendered by reference to specific embodiments thereof, which areillustrated in the appended drawings. It is appreciated that thesedrawings depict only illustrated embodiments of the disclosure and aretherefore not to be considered limiting of its scope. The disclosurewill be described and explained with additional specificity and detailthrough the use of the accompanying drawings in which:

FIG. 1 is a block diagram schematically illustrating a system for rapidextraction and analysis of soluble proteins from at least onemicroorganism for identifying the at least one microorganism;

FIG. 2 is a schematic representation of an exemplary mass spectrometersuitable for employment in conjunction with methods according to thepresent teachings, the mass spectrometer comprising a hybrid systemcomprising a quadrupole mass filter, a dual-pressure quadrupole ion trapmass analyzer and an electrostatic trap mass analyzer;

FIG. 3A is a flow diagram of a first method in accordance with thepresent teachings;

FIG. 3B is a flow diagram of an alternative method in accordance withthe present teachings;

FIG. 3C is a flow diagram of another alternative method in accordancewith the present teachings;

FIG. 3D and FIG. 3E illustrate a flow diagram of yet another alternativemethod in accordance with the present teachings;

FIG. 3F is a flow diagram of still yet another alternative method inaccordance with the present teachings;

FIG. 4A is an ESI mass spectrum via direct infusion of a typical E. coliextract;

FIG. 4B is a PTR product-ion mass spectrum generated by isolating ionsof the E. coli extract of FIG. 4A within a 2 Th mass window centered atm/z=750 Th and reacting the isolated ions with PTR reagent anions;

FIG. 5A is a mass spectrum of first-generation PTR product ionsgenerated by isolating ions of an E. coli extract within a mass windowof width 5 Th centered at 1200 Th and reacting the isolated ions withPTR reagent anions;

FIG. 5B is a mass spectrum of second-generation PTR product ionsgenerated by isolating ions of the first-generation PTR product ions ofFIG. 5A within a mass window of width 5 Th centered at 1320 Th andreacting the isolated first-generation product ions with PTR reagentanions a second time;

FIG. 6A is a mass spectrum of PTR product ions generated by isolatingions of an E. coli extract within a mass window of width 5 Th centeredat 640 Th and reacting the isolated ions with PTR reagent anions;

FIG. 6B is a mass spectrum of an isolated PTR product ion speciesselected from the product ion assemblage of FIG. 6A and having an m/zratio of 833 Th;

FIG. 6C is a mass spectrum of second-generation product ions generatedby collision-induced dissociation (CID) of the isolated PTR product ionspecies of FIG. 6B;

FIG. 6D is a mass spectrum of an isolated PTR product ion speciesselected from the product ion assemblage of FIG. 6A and having an m/zratio of 926 Th;

FIG. 6E is a mass spectrum of second-generation product ions generatedby collision-induced dissociation of the isolated PTR product ionspecies of FIG. 6D;

FIG. 6F is a mass spectrum of an isolated PTR product ion speciesselected from the product ion assemblage of FIG. 6A and having an m/zratio of 917 Th;

FIG. 6G is a mass spectrum of second-generation product ions generatedby collision-induced dissociation of the isolated PTR product ionspecies of FIG. 6F;

FIG. 7A is a schematic depiction of a method, in accordance with thepresent teachings, of improved-efficiency PTR conversion of ions of aselected analyte to an assemblage of PTR product ions by simultaneousisolation and reaction of multiple m/z ranges of electrospray-producedfirst-generation precursor ions;

FIG. 7B is a schematic diagram of isolation of a first randomly-chosenrange of electrospray-produced first-generation precursor ions for PTRreaction, as may be employed in an initial step of a method ofimproved-efficiency PTR conversion of ions;

FIG. 7C is a schematic depiction of recognition of two charge-statesequences of PTR product ions corresponding to different analytemolecules, as may be employed as an intermediate step of a method ofimproved-efficiency PTR conversion of ions;

FIG. 8 is a flow diagram of a method, in accordance with the presentteachings, of improved-efficiency PTR conversion of ions of a selectedanalyte to an assemblage of PTR product ions;

FIG. 9A is a full scan mass spectrum of first-generation ions generatedfrom eluate at a retention time of 10 min. and 30 s. during the courseof a ten-minute gradient reverse-phase liquid chromatography separationof an E. coli extract;

FIG. 9B is a PTR product ion spectrum generated by reacting sulfurhexafluoride for 10 ms with an isolated population of ions of the sampleof FIG. 9A within a 10 Th wide isolation window centered at 750 Th;

FIG. 10A is a full scan mass spectrum of first-generation ions generatedfrom eluate at a retention time of 42 min. and 30 s. during the courseof a sixty-minute gradient reverse-phase liquid chromatographyseparation of an E. coli extract;

FIG. 10B is a PTR product ion spectrum generated by reacting sulfurhexafluoride for 10 ms with an isolated population of ions of the sampleof FIG. 10A within a 10 Th wide isolation window centered at 750 Th;

FIG. 11A is a full scan mass spectrum of first-generation ions generatedfrom eluate at a retention time of 18 min. and 9 s. during the course ofa thirty-minute gradient reverse-phase liquid chromatography separation;

FIG. 11B is a PTR product ion spectrum generated by reaction of PTRreagent ions with an isolated population of ions of the sample of FIG.11A within a 10 Th wide isolation window centered at 750 Th;

FIG. 11C is a full scan mass spectrum of first-generation ions generatedfrom eluate at a retention time of 22 min. and 27 s. during the courseof the same thirty-minute gradient reverse-phase liquid chromatographyseparation of which the earlier elution results are plotted in FIG. 11A;

FIG. 11D is a PTR product ion spectrum generated by reaction of PTRreagent ions with an isolated population of ions of the sample of FIG.11C within a 10 Th wide isolation window centered at 750 Th;

FIG. 12A is a schematic illustration of simple intensity-threshold-baseddata dependent mass spectral analysis of two analytes exhibitingwell-resolved chromatographic peaks;

FIG. 12B is a schematic illustration of a portion of a chromatogram withhighly overlapping elution peaks, both of which are above an analyticalthreshold;

FIG. 12C is an illustration of multiple interleaved mass spectral peaksof two simultaneously eluting biopolymer analytes;

FIG. 13 is a set of chromatograms collected from a single liquidchromatography-mass spectrometry experimental run of an E. Coli extract,including a total ion current chromatogram (top curve) and alsoillustrating various extracted ion chromatograms (lower curves) thatcontribute to the total ion current, each extracted ion chromatographrepresenting a respective m/z ratio range;

FIG. 14 is a flowchart of a general set of steps employed by variousmethods in accordance with the present teachings;

FIG. 15 is a flowchart of a method to convert experimentally measuredmass spectral centroids to an occupancy array, with the indices that arerelated to one another by different charge states encapsulated in amatrix, in accordance with the present teachings;

FIG. 16 is a flowchart of a method in accordance with the presentteachings for constructing a Boolean occupancy array within amathematically transformed mass-to-charge space from experimentallydetermined mass spectral centroid data;

FIG. 17A and continuation FIG. 17B, is a flowchart of a method inaccordance with the present teachings for assigning tentative chargestates for a plurality of experimentally determined mass spectralcentroids;

FIG. 18 is a flowchart of a method in accordance with the presentteachings for adjusting a set of previously tentatively assigned chargestates such that the resulting final assigned charge states areself-consistent;

FIG. 19 is a flowchart of a method in accordance with the presentteachings for decomposing a set of experimentally determined centroidshaving assigned charge states into analyte-specific clusters;

FIG. 20A and continuations FIGS. 20B, 20C and 20D, is a table showingtypical molecular weights, expected number of C¹³ atoms in the mostabundant isotope (mode), expected average number of C¹³ atoms among allisotopes and the difference between the expected average number and themode, as they vary with the total number of C¹² atoms in a protein;

FIGS. 21A, 21B, 21C and 21D are depictions of computer screen userinterfaces which may be employed in conjunction with user control of andinformation display from computer software that employs methods inaccordance with the present teachings;

FIG. 22A is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, calculated from a mass spectrum of a five-component proteinmixture consisting of cytochrome-c, lysozyme, myoglobin, trypsininhibitor, and carbonic anhydrase;

FIG. 22B is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, the display illustrating an expanded portion of thedecomposition results shown in FIG. 22A;

FIG. 22C is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, the display illustrating an even-further expanded portion ofthe decomposition results shown in FIG. 12B;

FIG. 23A is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, the display illustrating peak cluster decomposition resultscalculated from a single-stage mass spectrum of a crude extract from thebacterium E. coli directly infused into a mass spectrometer;

FIG. 23B is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, the display illustrating an expanded portion of thedecomposition results shown in FIG. 23A;

FIG. 23C is a depiction of the mass spectral data whose peak clusterdecomposition is shown in FIGS. 23A-23B, showing peak positions andcharge-state assignments as provided by a conventional mass spectralpeak analysis computer program;

FIG. 23D is a depiction of the mass spectral data whose peak clusterdecomposition is shown in FIGS. 23A-23B, showing charge-stateassignments as provided by methods in accordance with the presentteachings;

FIG. 24A is a depiction of a mass spectrum of an intact antibody havingvarying degrees of glycosylation (main plot) also showing (inset) anexpanded portion of the spectrum illustrating the different glycoformsof the antibody;

FIG. 24B is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, calculated from the mass spectral data shown in FIG. 24A,showing the calculated molecular weights of the four decomposedglycoforms of the antibody ranging from 148378 Da to 148763 Da;

FIG. 25A is a depiction of an MS² spectrum of the protein carbonicanhydrase II, generated by collision-induced dissociation of the +26charge state of the protein occurring at m/z=807.00 Da, showing peakassignments as determined by a conventional mass spectral analysismethod;

FIG. 25B is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, calculated from the MS² mass spectral data shown in FIG. 25A;

FIG. 25C is a depiction of a second MS² spectrum of the protein carbonicanhydrase II, generated by collision-induced dissociation of the +21charge state of the protein at m/z=1001.00 Da, showing peak assignmentsas determined by a conventional mass spectral analysis method; and

FIG. 25D is a depiction of a computer screen information displayillustrating peak cluster decomposition results, as generated bycomputer software employing methods in accordance with the presentteachings, calculated from the MS² mass spectral data shown in FIG. 25C.

MODES FOR CARRYING OUT THE INVENTION

The following description is presented to enable any person skilled inthe art to make and use the invention, and is provided in the context ofa particular application and its requirements. Various modifications tothe described embodiments will be readily apparent to those skilled inthe art and the generic principles herein may be applied to otherembodiments. Thus, the present invention is not intended to be limitedto the embodiments and examples shown but is to be accorded the widestpossible scope in accordance with the claims. The particular featuresand advantages of the invention will become more apparent with referenceto the appended FIGS. 1-11D taken in conjunction with the followingdescription in the main body of this document as well as FIGS. 12A-25Dtaken in conjunction with the description in the appendix to thisdocument.

Referring now to FIG. 1, a system 100 for extraction of proteins fromone or more microorganisms, detection of the proteins, andidentification of the one or more microorganisms is schematicallyillustrated. The system 100 includes a sample handling device 115, asample 110 that is accessible by the sample handling device 115, andsources of reagents, buffers, and the like 120, these sources beingfluidly coupled to the sample handling device 115 by various tubing orother transfer lines. The system 100 further includes a first and,optionally, a second sample-purification device 135 (such as a solidphase extraction cartridge) configured for cleaning up samples (e.g.,desalting, removing contaminants, concentrating proteins) and anoptional chromatography column 140 that may be configured for at leastpartially purifying a sample 110 by liquid chromatography prior tomass-spec analysis. At least one sample-purification device 135 cancomprise an in-line size exclusion chromatography column that can beused to not only remove salts but small molecules and lipids as well.The sample 110, the first and optional second sample-purificationdevices 135, and the optional chromatography column 140 are in fluidcommunication with a fluid handling pump 130, the various reagents,buffers and other fluids 120, and a mass spectrometer 150.

The sample handling device 115 is capable of preparing a range of sampletypes containing one or more microbes and delivering a soluble proteinfraction extracted from the microbes to the mass spectrometer 150 foranalysis. A sample 110 may be of any type suspected to contain one ormore microorganisms including, without limitation, isolated coloniesfrom a culture plate, cells from liquid growth medium, blood, bloodculture, saliva, urine, stool, sputum, wound and body site swabs, soil,food, beverage, water, air, and environmental surface swabs.

The sample handling device 115 may include one or more of a celldisruption means, a robotic liquid handling means, a centrifuge,filtration means, an incubator, mixing means, a vacuum pump, a fluidpump, and reagents 120 that can be used for disruption of microbes andisolation of a soluble protein fraction. Disruption of bacterial,fungal, mycoplasma cells, viruses, and the like may be achieved bymechanical, chemical, enzymatic and other means as are commonly known inthe art. Mechanical approaches include bead beating, use of pressurelike French press and the like, sonication or other methods known in theart. Chemical methods include exposure to chaotropes such as urea,thiourea, or guanidine HCL to lyse the microbial cells and solubilizetheir contents. Alternatively, organic acid/solvents mixtures may beutilized to disrupt cells. Enzymatic methods include using lysozyme,lysostaphin or other lytic enzymes to form “holes” in the bacterial cellwalls that allow the contents to leak out into the surrounding solution.

As illustrated in FIG. 1, the system 100 further includes an optionalcontrol unit 160 that can be linked to various components of the system100 through linkages 170 a-170 d. For example, the control unit 160 canbe linked to the sample 110 to control sample application, the reagents120 to control the application of various reagents, the pump 130 tocontrol fluid handling, flow rates, etc., to the sample handling device115 to control sample preparation, and to the mass spectrometer 150 tocontrol mass spectrometry parameters. In the illustrated embodiment, thecontrol unit 160 can also serve as a data processing unit to, forexample, process data from the mass spectrometer 150 or to forward thedata to server(s) for processing and storage (the server is not shown inFIG. 1). Control unit 160 can also determine molecular weights andcharge states of any generation of PTR product ions for MS/MS, MS^(n,)or molecular weight determination in real time. The Control Unit 160 canalso be used to automatically forward the results to health careprofessionals.

In some embodiments, the system 100 is designed to be used by aclinician or a general laboratory technician who is not necessarilyexpert in all aspects of sample preparation, LC-MS operations, LC-MSmethods development, and the like. As such, the control unit 160 can bedesigned to encapsulate the data system environment by providing a userwith a simplified application interface that can be used to initiate andmonitor essentially all aspects of assaying a sample 110 withoutrequiring the user to interact with the overall hardware and controlsystems of the system 100. The control unit 160 is therefore configuredto provide a degree of separation between the user and the underlyingservices that control devices, data files and algorithms for translatingdata to a user readable form. That is, the control unit 160 eliminatesthe need for the user to be aware of or in control of hardware foranalyzing clinical samples and provides a simplified interface to sendand receive information from the mass spectrometer.

The control unit 160 may be configured to internally monitor each sampleanalysis request and is capable of tracking the analysis request fromstart to finish through the system 100. Once data for a sample 110 isbeing acquired or has been acquired by the system 100, the control unit160 may be configured to automatically start post processing the databased on the type of assay selected by the user. Most importantly, thecontrol unit 160 can be configured to process data in real time duringthe acquisition process. Here results are returned to the user inreal-time that include microbial identification, virulence andresistance characterization, strain matching information, and data onantibiotic susceptibility testing. Moreover, the control unit 160 can beconfigured to automatically select post-processing parameters based onthe type of assay selected by the user, further reducing the need forthe user to interact with the system once the assay has been selectedand started for analysis. The control unit 160 can be designed as alayer that fits between the system 100 and the user to reduce thecomplexity needed to set up sample assays for acquisition. The controlsystem 160 can also be configured to return only the most relevant datato the user to avoid overwhelming the user with extraneous information.

In one embodiment, the system 100 can further include a sample detectiondevice (not pictured) operably coupled to or integrated with the samplehandling device 115. The sample detection device can work with thesample handling device 115 or independently of the sample handlingdevice 115 perform at least one of the following functions: i. identifysamples entering the system; ii. identify assay types for the samplesentering the system; iii. select an assay protocol based on theanticipated assay type and/or analyte of interest; iv. direct the samplehandling device and/or the control system to initiate analysis of theanalyte of interest in the sample; v. direct the control system toselect one or more reagents based upon the assay protocol selected forthe type of assay and/or analyte of interest; vi. direct the controlsystem to select a liquid chromatography mobile phase condition basedupon the assay protocol selected for the type of assay and/or analyte ofinterest and cause the liquid chromatography system to perform the assayand/or purify the analyte of interest; vii. direct the control system toselect a mass spectrometer setting based upon the assay protocolselected for the assay type and/or analyte of interest and cause themass spectrometer to create mass spectral data associated with theselected assay type and/or analyte of interest; and viii. direct thecontrol system to analyze the mass spectral data associated with theselected assay type and/or analyte of interest to identify the presenceand/or concentration of the analyte of interest.

The sample, or the processed sample, may be cleaned up and or purifiedprior to analysis by mass spectrometry. Such purification, or sampleclean-up, may refer to a procedure that removes salts or lipids from thecrude cell extract, or to a procedure that enriches one or more analytesof interest relative to one or more other components of the sample. Italso may refer to sample processing and clean-up in a separatelaboratory that has biosafety level-three facilities for handlingmycobacteria or filamentous fungi. In this embodiment samples aretransferred to the system and can be analyzed as described previously.In one embodiment, such purification, or sample clean-up, may beaccomplished by a solid phase extraction device, in-line size exclusionchromatography and/or the optional chromatography column 140.

In one embodiment, the first and/or second sample-purification device135 may include a solid phase extraction (SPE) cartridge. In someembodiments, the SPE cartridge may be in line directly with the highresolution/high mass accuracy mass spectrometer 150. In one embodiment,the SPE cartridge may be a polypropylene tip with a small volume ofsilica or other sorbent containing bonded C₄, C₈ or C₁₈ or otherfunctional groups immobilized in the cartridge, for example, a StageTip™cartridge (Thermo Fisher Scientific). In alternative embodiments,polymeric sorbents or chelating agents may be used. The bed volume maybe as small as 1 μL or less but greater volumes may also be used. Theapparatus and method are well suited to the complex samples derived fromthe microbial cells because each SPE cartridge is used only once,minimizing carryover problems from one sample to another.

In one embodiment, a sample-purification device 135 may be an in-linesize-exclusion chromatography column designed to remove salts, smallmolecules, and lipids from the sample 110. The approach can be used toseparate medium and large molecular weight proteins as well. Phases areselected to be compatible with partial (i.e., less than 100 percent)organic solutions and organic acids. Phases can accommodate protein sizedistributions that differ in molecular weight from 10³ to 10⁸ Da. Flowrates are adjusted in real time to effect separation of intact proteinsfrom small molecules with separation flow rates typically much less thanthe higher flow rates used to remove small molecules, lipids, and saltsfrom the system. In this embodiment, a sample-purification device 135may also be heated to facilitate faster diffusion rates for intactproteins, thus significantly shortening run times. The flow of mobilephase through a sample-purification device 135 may also be divertedduring a portion of the clean-up process to remove certain impuritiesfrom the flow stream and prevent them from entering the massspectrometer 150.

In one embodiment, the optional chromatography column 140 may include acolumn configured for at least partial chromatographic separation of theproteins in the sample. The stationary phase in the chromatographycolumn may be porous or non-porous silica or agarose particles, or amonolithic material polymerized or otherwise formed inside the column.The stationary phase may be coated with an appropriate material such asC₁₈, C₈, C₄ or another suitable derivative, or contain cation exchangeror other material, or the combination of the above to facilitate theseparation of the proteins, and such material may be chemically bondedto the particles or monolith inside the column. Particle sizes typicallyrange from about 1.5 μm to 30 μm. Pore sizes can range from 50 to 300angstroms. Inside diameters of columns typically range from about 50 μmto 2.1 mm, and column length from about 0.5 cm to 25 cm, or other. Themobile phase or eluent may be a pure solvent, or a mixture of two ormore solvents, and may contain added salts, acids and/or other chemicalmodifiers. The proteins are separated on the column based on one or morephysiochemical properties, including size, net charge, hydrophobicity,affinity, or other physiochemical properties. Chromatographic separationmethods include one or more of ion exchange, size exclusion, HILIC,hydrophobic interaction, affinity, normal-phase, or reverse-phasechromatography.

Additional methods of purifying the samples may include, withoutlimitation, liquid chromatography, HPLC, UHPLC, precipitation,solid-phase extraction, liquid-liquid extraction, dialysis, affinitycapture, electrophoresis, filtration, ultra-filtration or other suitablemethods known in the art for purification.

Various methods have been described involving the use of HPLC for sampleclean-up prior to mass spectrometry analysis. One of skill in the artcan select HPLC instruments and columns that are suitable for use in theinvention. The chromatographic column typically includes a medium (i.e.,a packing material) to facilitate separation of chemical moieties inspace and time. The medium may include very small particles, which mayhave a bonded surface that interacts with the various chemical moietiesto facilitate separation of the analytes of interest. One suitablebonded surface is a hydrophobic bonded surface such as an alkyl bondedsurface. Alkyl bonded surfaces may include C₄, C₈, or C₁₈ bonded alkylgroups. In addition, monolithic and other phases known in the state ofthe art may be used as well. The chromatographic column includes aninlet port for receiving a sample and an outlet port for discharging aneffluent that includes the fractionated sample. For example, a testsample may be applied to the column at the inlet port, eluted with asolvent or solvent mixture, and discharged at the outlet port. Inanother example, more than one column may be used sequentially or as atwo-dimensional (2D) chromatography system wherein a test sample may beapplied to a first column at the inlet port, eluted with a solvent orsolvent mixture onto a second column, and eluted with a solvent orsolvent mixture from the second column to the outlet port. Differentsolvent modes may be selected for eluting the analytes. For example,liquid chromatography may be performed using a gradient mode, anisocratic mode, or a polytyptic (i.e. mixed) mode.

FIG. 2 is a schematic depiction of an exemplary mass spectrometer 150 awhich may be employed as the mass spectrometer 150 of FIG. 1. The massspectrometer illustrated in FIG. 2 is a hybrid mass spectrometer,comprising more than one type of mass analyzer. Specifically, the massspectrometer 150 a includes an ion trap mass analyzer 216 as well as anOrbitrap™ analyzer, which is a type of electrostatic trap mass analyzer.Since, as will be described below, various analysis methods inaccordance with the present teachings employ multiple mass analysis dataacquisitions, a hybrid mass spectrometer system can be advantageouslyemployed to improve duty cycles by using two or more analyzerssimultaneously. The Orbitrap™ mass analyzer 212 employs image chargedetection, in which ions are detected indirectly by detection of animage current induced on an electrode by the motion of ions within anion trap.

In operation of the mass spectrometer 150 a, an electrospay ion source201 provides ions of a sample to be analyzed to an aperture of a skimmer202, at which the ions enter into a first vacuum chamber. After entry,the ions are captured and focused into a tight beam by a stacked-ringion guide 204. A first ion optical transfer component 203 a transfersthe beam into downstream high-vacuum regions of the mass spectrometer.Most remaining neutral molecules and undesirable high-velocity ionclusters, such as solvated ions, are separated from the ion beam by acurved beam guide 206. The neutral molecules and ion clusters follow astraight-line path whereas the ions of interest are caused to bendaround a ninety-degree turn by a drag field, thereby producing theseparation.

A quadrupole mass filter 208 of the mass spectrometer 150 a is used inits conventional sense as a tunable mass filter so as to pass ions onlywithin a selected narrow m/z range. A subsequent ion optical transfercomponent 203 b delivers the filtered ions to a curved quadrupole iontrap (“C-trap”) component 210. The C-trap 210 is able to transfer ionsalong a pathway between the quadrupole mass filter 208 and the ion trapmass analyzer 216. The C-trap 210 also has the capability to temporarilycollect and store a population of ions and then deliver the ions, as apulse or packet, into the Orbitrap™ mass analyzer 212. The transfer ofpackets of ions is controlled by the application of electrical potentialdifferences between the C-trap 210 and a set of injection electrodes 211disposed between the C-trap 210 and the Orbitrap™ mass analyzer 212. Thecurvature of the C-trap is designed such that the population of ions isspatially focused so as to match the angular acceptance of an entranceaperture of the Orbitrap™ mass analyzer 212.

Multipole ion guide 214 and optical transfer component 203 b serve toguide ions between the C-trap 210 and the ion trap mass analyzer 216.The multipole ion guide 214 provides temporary ion storage capabilitysuch that ions produced in a first processing step of an analysis methodcan be later retrieved for processing in a subsequent step. Themultipole ion guide 214 can also serve as a fragmentation cell. Variousgate electrodes along the pathway between the C-trap 210 and the iontrap mass analyzer 216 are controllable such that ions may betransferred in either direction, depending upon the sequence of ionprocessing steps required in any particular analysis method.

The ion trap mass analyzer 216 is a dual-pressure quadrupole linear iontrap (i.e., a two-dimensional trap) comprising a high-pressure lineartrap cell 217 a and a low-pressure linear trap cell 217 b, the two cellsbeing positioned adjacent to one another separated by a plate lenshaving a small aperture that permits ion transfer between the two cellsand that presents a pumping restriction and allows different pressuresto be maintained in the two traps. The environment of the high-pressurecell 217 a favors ion cooling, ion fragmentation by eithercollision-induced dissociation or electron transfer dissociation orion-ion reactions such as proton-transfer reactions. The environment ofthe low-pressure cell 217 b favors analytical scanning with highresolving power and mass accuracy. The low-pressure cell includes adual-dynode ion detector 215.

The use of either a step of electron transfer dissociation or protontransfer reaction within a mass analysis method requires the capabilityof causing controlled ion-ion reaction within a mass spectrometer.Ion-ion reactions, in turn, require the capabilities of generatingreagent ions and of causing the reagent ions to mix with sample ions.The mass spectrometer 150 a, as depicted in FIG. 2, illustrates twoalternative reagent-ion sources, a first reagent-ion source 299 adisposed between the stacked-ring ion guide 204 and the curved beamguide 206 and a second reagent-ion source 299 b disposed at the oppositeend of the instrument, adjacent to the low-pressure cell 217 b of thelinear ion trap mass analyzer 216. Generally, any particular system willonly include one reagent ion source at most. However, two differentreagent ion sources are depicted and discussed here for illustrativepurposes. Although the following discussion is directed to reagent ionsources for PTR, similar discussion may apply to ETD reagent ionsources.

A first possible reagent ion source 299 a may be located between thestacked ring ion guide 204 and the curved beam guide 206. The reagention source 299 a comprises a glow discharge cell comprising a pair ofelectrodes (anode and cathode) that are exposed to a reagent gas conduit298 a that delivers the reagent gas from a reagent liquid (or solid)reservoir 297 a having a heater that volatilizes the reagent compound.When a high voltage is applied across the electrodes, glow discharge isinitiated which ionizes the reagent flowing between the electrodes.Reagent anions from the glow discharge source are introduced into theion optics path ahead of the quadrupole mass filter 208 within whichthey may be m/z selected. The reagent ions may then be accumulated inthe multipole ion guide 214, and subsequently transferred into the highpressure cell 217 b of the dual-pressure linear ion trap 216 withinwhich they are made available for the PTR reaction. The reactionproducts may be directly transferred to the low pressure cell 217 a orto the Orbitrap™ mass analyzer 212 for m/z analysis.

A possible alternative reagent ion source 299 a may be located adjacentto the low pressure linear trap cell 217 b where it may comprise anadditional high-vacuum chamber 292 from which reagent ions may bedirected into the high pressure cell 217 b through an aperture inbetween chamber 292 and the high-pressure cell. In operation, gaseousreagent compound is supplied from a reagent liquid (or solid) reservoir297 b having a heater that volatilizes the reagent compound and isdirected through a reagent gas conduit 298 b that delivers the reagentgas into a partially confined ion generation volume 296. In operation,thermionic electrons supplied from an electrically heated filament 294are directed into the ion generation volume 296 with a certainpre-determined energy by application of an electrical potential betweenthe filament 294 and an accelerator electrode (not shown). The suppliedenergetic electrons cause ionization of the reagent gas so as togenerate reagent ions. The reagent ions may then be guided into the highpressure cell 217 b by ion optical transfer component 203 a under theoperation of gate electrodes (not shown).

Exemplary methods in accordance with the present teachings areschematically illustrated in the flow diagrams shown in FIGS. 3A-3F.FIG. 3A schematically illustrates a first such exemplary method, method300, for monitoring for the presence of and, optionally, quantifying,certain specific targeted analyte proteins or peptides in a biologicalsample. For example, the sample may be a sample of microorganisms. Theinitial steps 302, 304 and 306 of the method 300 are the steps of celllysis (if the sample is a sample of microorganisms or other cells) andextraction, solid-phase clean-up, or size-exclusion chromatography andchromatographic separation, respectively, as described above. Step 302may only be applicable if the sample comprises a tissue sample, abacterial cell sample, a cell sample of another microorganism, oranother form of cell sample. In some experimental situations, theextracted sample may be directly infused into a mass spectrometer in thesubsequent sample introduction step 308; thus, the steps 304 and 306 areshown by dashed lines as being optional. Samples may also be preparedusing offline approaches including dialysis, or other techniques knownin the state of the art. However, in many other experimental situations,the steps 304 and 306 are useful so as to at least partially purify thesample prior to mass-spectral analysis.

When an analysis must be completed according to time constraints, as insome clinical applications, the required time for the analysis may beshortened by employing either a SPE step 304, a time-compressedchromatography step as described in U.S. Pat. No. 5,175,430 to inventorEnke, or the method of “Fast Partial Chromatographic Separation” (FPCS)in the chromatography step 306 as described in international (PCT)patent application publication WO 2013/166169 A1. Generally, inperforming FPCS, a crude extract of microbial cells containing a complexmixture of various organic and inorganic analytes (small organicmolecules, proteins and their naturally occurring fragments, lipids,nucleic acids, polysaccharides, lipoproteins, etc.) is loaded on achromatographic column and subjected to chromatography. However, insteadof allowing a gradient to elute each analyte separately (ideally, oneanalyte per chromatographic peak), the gradient is intentionallyaccelerated to the extent that substantially no chromatographic peaksobtained for example approximately eight minutes or less, and preferablyfive minutes or less instead of a much longer run time that would berequired to obtain a baseline separation. In the FPCS separation, manyanalytes are intentionally co-eluted from the column at any given timeaccording to their properties and the type of chromatography (reversephase, HILIC, etc.) used. Partial or incomplete separation may be alsoaccomplished by other methods known to one skilled in the art, includingbut not limited to the use of mobile phase solvents and/or modifiersthat reduce retention of compounds on the column, selection ofstationary phase media that reduce retention of compounds on the column(including particle size, pore size, etc.), operation of thechromatographic system at higher flow rate, operation of thechromatographic system at an elevated temperature, or selection of adifferent chromatographic separation mode (i.e., reversed-phase, sizeexclusion, etc.). The FPCS technique yields few or, possibly, noresolved chromatographic peaks across the whole gradient. Thus,substantially the only relevant information derived from a chromatogramis the time of elution from the column. Each mass spectrum that isrecorded represents a “subset” of co-eluting analytes that is thenionized, separated in the mass analyzer and detected.

In step 308 (FIG. 3A), the sample is introduced into a massspectrometer. The sample may be provided as the eluate material thatemerges from an SPE cartridge, a chromatography apparatus or,alternatively, by direct infusion of the eluate solution. Upon beingprovided to the mass spectrometer, the sample compounds are ionized(step 308) by an electrospray ionization source of the massspectrometer. These electrospray-generated ions are herein referred toas “first-generation” ions. At this juncture, a full or segmented MS¹scan may optionally be performed (step 309) in order to identify theprotein-rich regions in m/z space. (Note that in this document, the term“scan” may be taken to generally refer to a mass spectrum when used as anoun or, alternatively, to the acquisition of a mass spectrum, when usedas a verb). In a preferred embodiment, the MS¹ scan can be obtained overthe full mass range of the mass spectrometer instrument in order to beable to subsequently choose, in data-dependent or independent fashion,an information-rich portion of the spectrum for isolation (step 310).However, in the case of a targeted analysis, the MS¹ scan may beunnecessary and execution of the method 300 may proceed directly to step310, in which a subset of the ions is then isolated for further reactionand analysis. When targeted analysis is employed, the isolationperformed in step 310 may be such that ions within a certainpre-determined m/z range or possibly multiple pre-determined m/z rangesare retained for the subsequent reaction and analysis whereas ionsoutside the pre-determined m/z range or ranges are discarded. Thepre-determined m/z range or ranges are chosen so as to correspond topreferably known m/z ratios of targeted analyte proteins or peptideswhose presence or quantity is detected or monitored in the execution ofthe method.

Generally, the isolation of step 310 may be performed, in known fashion,by introducing the ions from the ion source into an ion trap—such as athree-dimensional ion trap, a curved ion trap (sometimes referred to asa “C-Trap”) a single segment linear ion trap, multiple segmented linearion trap, multipole ion guide or quadrupole mass filter—and thenresonantly ejecting the ions whose m/z ratios are outside of the desiredrange by applying a supplemental AC voltage across pairs of electrodesof the ion trap or applying the appropriate RF/DC voltage ratios toisolate the ion population of interest. In some embodiments, thefrequency of the supplemental voltage may be swept through variousfrequencies such that the ions are ejected in sequence according totheir m/z ratios. In such cases, the ions may be detected as they areejected so as to generate a mass spectrum of the original set of ions.However, since a mass spectrum may not be required at this stage, thesupplemental AC voltage may be alternatively applied as a combination ofsuperimposed frequencies that are chosen so as to cause essentiallysimultaneous ejection of the ions whose whose m/z ratios are outside ofthe desired range. In some embodiments, the combination of superimposedfrequencies may be provided with multiple segments of missingfrequencies (i.e., “notches”) such that ions comprising two or morenon-contiguous m/z ratio ranges are simultaneously isolated within thetrap. Each one of the non-contiguous m/z ratio ranges may correspond toa preferably known m/z ratio of a respective unique targeted analyteprotein or peptide. The applied RF/DC voltage ratios of a quadrupolemass filter may also be used to isolate the defined or targeted massranges of interest. Particular m/z ranges of the first-generation ionsare selected by a single or series of fixed RF/DC voltage ratios inorder to select the appropriate mass isolation windows. The instrumentalconfiguration employed in this case may be a hybrid mass spectrometerinstrument comprising a quadrupole, a C-trap, an Orbitrap™ massanalyzer, and a high energy collision cell (HCD) where the isolated ionpopulation can be stored in either the C-trap or HCD cell for PTRexperiments. The isolated population or populations of thefirst-generation ions are herein referred to as “precursor” ions,because these ions will be subjected to subsequent ion-ion reactions orto fragmentation.

In a preferred embodiment, the isolation of the precursor ion populationmay be performed in a first segment of a segmented linear ion trap.After isolation of the desired ion population, the multiply-chargedprotein ion population may be advantageously moved to another segment ofthe linear ion trap. These steps can be repeated multiple times forisolated defined ranges of precursor ions prior to the PTR process.

Next, anions are generated using either a rhenium-based filament withchemical ionization or glow discharge ionization source from a suitablehigh electron affinity based gaseous reagent. Ionization can beperformed using nitrogen, methane, isobutane, or other known gases inthe state of the art. The anion reagent may be a gas at room temperatureor may be a liquid with sufficient vapor pressure to produce an excessof anions which will drive the PTR process under pseudo-first orderreaction conditions. The anions are then transferred from the sourceregion to the segmented linear trap whereby the specific anion reagentis mass isolated using supplemental AC voltages as described above. Theanion source can be in-line with the electrospray source or mounted onthe opposite end of the segmented linear ion trap. Alternatively, aquadrupole mass filter can perform the anion isolation as well with thesubsequent PTR process occurring in the C-trap or HCD cell of theinstrument.

In step 312 of the method 300 (FIG. 3A), the ions which weremass-isolated in step 310 (i.e., “precursor” ions) are subjected to aproton transfer reaction in which a reagent anion species is reacted fora specified time period with the sample precursor ions in the ion trapso as to extract protons from the precursor cations. In one embodiment,the multiply-charged precursor ion population and the singly-chargedanion population are reacted by adjusting the DC voltage offsets of thesegmented linear ion trap so as to store both the multiply-chargedpositive ions with the singly charged anions to facilitate the PTRprocess. The reagent anions are chosen such that, in this instance, thereagent anions behave as a Brønsted-Lowry base and such that theprecursor ions behave as one or more Brønsted-Lowry acids. The reagentanions are formed by separate ionization of a suitable reagentgas/liquid with sufficient vapor pressure, that includes but is notlimited to sulfur hexafluoride, perfluoro-1,3-dimethyl cyclohexane,perfluorodecalin, and perfluoroperhydrophenanthrene. After allowing thereaction to proceed for a specified time, a supplementary AC voltage isapplied across electrodes of the ion trap so as to eject the reagentanions, thereby leaving product ions and, possibly, some residualprecursor ions within the ion trap.

In the opposite polarity experiment, multiply-charged anions derivedfrom proteins or other biomolecules can also be reacted withsingly-charged cations. A variety of sources can be employed to generatesingly-charged cations including electron, chemical, and electrosprayionization processes. These reactions follow the same reaction kineticsdescribed previously. Typical reagent cations have included pyridine,benzo(f)quinolone, and the noble gases argon and xenon. In addition,multiply-charged proteins of opposite polarity have also been reacted aswell as the multiply-charged anions from nucleic acids with themultiply-charged cations of proteins.

In step 314 of the method 300 (FIG. 3A), a mass spectrum is obtained ofthe product ions from the PTR process retained in the ion trap over afull range of m/z ratios of interest. The mass spectrum may be obtained,in known fashion, by detecting ions that are sequentially ejected fromthe 3D or linear ion trap in order of their m/z ratios. Alternatively,the ions may be directed to a different mass analyzer of the massspectrometer, such as a Time-of-Flight (TOF) mass analyzer or anOrbitrap™-type of electrostatic trap mass analyzer, to be analyzed withgreater accuracy or mass resolution then may be available by sequentialscanning of the ion trap. Further, by directing the product ions to aseparate analyzer, the ion trap may be re-filled with a new sample ofprecursor ions while the mass analysis is being performed. If theaccurate mass analyzer is of a type—such as an FT-ICR mass analyzer oran Orbitrap™ mass analyzer—that detects image currents produced bycyclic ion motion within an ion trap, then the PTR reaction steps mayadvantageously reduce collision cross sections of targeted protein orpolypeptide molecules such that these molecules remain stable in thetrap for a sufficient length of time to generate high-quality massspectra. Also, the PTR product ions will have less kinetic energy whenleaving the high pressure C-trap region upon their transfer to theOrbitrap™ mass analyzer. Due to the PTR process, the resulting production population will be fully desolvated which will improve the qualityof the resulting mass spectrum.

In step 316 of the method 300, the mass spectrum generated by the massanalysis performed in step 314 is automatically examined so as torecognize one or more individual series of related m/z ratios, whereineach m/z ratio of a series represents a respective different chargestate—that is, a different degree of protonation—of a single intactprotein or polypeptide molecule. For example, see FIG. 7C which depictstwo different series of lines, represented by the envelope 905 and theenvelope 906, respectively. After ionization as well as subsequent tothe PTR reaction, each protein or polypeptide molecule, M, of massm_(p), is represented as at least one (and likely several different)protein or polypeptide cation species. Each such cation species of arelated series formed from the particular molecule, M, may berepresented by the chemical formula (M+zH)^(z+), where the integer, z,is the number of protons adducted to the original molecule or is thenumber of protons remaining on the protein after the PTR step. In thisexample, considering only monoisotopic ions, the mass-to-charge ratio,(m/z)_(ion), is thus given by:

(m/z)_(ion)≈(m _(p) +z×1.007)/z≈(m _(p) +z)/z≈m _(p) /z   (Eq. 1)

where the final approximation results from the fact that m_(p)>>z.Accordingly, such series of ion species representing only differentstates of protonation may be readily recognized by using automatedsoftware in real time to determine the monoisotopic ions. Once suchseries have been recognized, the molecular mass, m_(p), of the parentprotein or polypeptide molecule may be discerned in real time. Similarapproaches can be applied to larger molecular weight molecules usingaverage or monoisotopic mass as well. The automatic examination of themass spectral data and recognition of one or more individual series ofrelated m/z ratios may be performed by any one of many known massspectral data analysis programs or software packages designedspecifically for this purpose. However, for use in clinical applicationsor other time-critical applications, this automatic examination ispreferably performed by an optimized computational method such as thecomputational methods that are described in detail in the appendix tothis document.

The m/z values generated by the PTR process or, alternatively, themolecular weights obtained from the PTR product ions can then besearched against a database containing tabulated values of m/z values ormolecular weights of proteins or polypeptides (step 402 of FIG. 3A) soas to recognize the presence of such analytes in the sample. If thesample is derived from a microorganism, then the database may relate toindividual pathogen standards that contain the observed m/z values ormolecular weights from known reference standards/patient samples. Insuch cases, by matching these m/z values or molecular weights from adatabase containing individual referenced pathogens, a small subset ofpossible pathogen identifications is obtained. The subset can be limitedby determining a particular mass accuracy, weighting the intensities ofthe individual peak, and/or by weighting the molecular weight values bymass in a given scoring system.

In certain cases of microorganism studies, the m/z or molecular weightmatches may provide a direct match to a particular pathogenidentification which may be determined automatically (e.g, step 404 ofFIG. 3A.). However, in all probability, the m/z molecular weightinformation will reduce substantially the number of possible pathogenidentifications that can be unequivocally identified using tandem massspectrometry. This process was originally described for use inconjunction with the steps 302, 304, 306, and 308-310 in international(PCT) patent application publication WO 2013/166169 A1. Additionally,Bayesian, logistic regression, or decision tree based methods can beemployed to further refine the identification of the pathogen. In apreferred embodiment, this m/z or molecular weight search is performedin real time during data acquisition (i.e., as the sample is beinganalyzed). Alternatively, the search may be performed post-acquisition(i.e., after the sample has been analyzed) as well. The comparison of asmall number of m/z values or molecular weights (3-10) of proteins to areference database will generally be sufficient to significantly reducethe candidate number of pathogen identifications to five or less.

Optionally, execution of the method 300 may return back to any of steps302, 308 or 310 after execution of step 402 as indicated by dashedarrows in FIG. 3A. This same option is also applicable to method 370illustrated in FIG. 3B, the method 380 illustrated in FIG. 3C, themethod 390 illustrated in FIGS. 3D-3E and the method 395 illustrated inFIG. 3F. Returning to step 302 corresponds to repeating the entirety ofthe method 300 in the analysis of a different sample. Returning to step308 corresponds to introducing a different portion of a same sample intothe mass spectrometer system and repeating the mass spectral analysis onthe different portion. In some cases, the different portion of the samesample may comprise a different chemical fraction of the same sample(that is, it may comprise a different composition from the prioranalyzed portion), if the sample is being chemically fractionated, e.g.,by chromatography (step 306) or by some other chemical fractionationtechnique. In some cases, the different portion of the sample maycomprise the same composition as the prior analyzed portion (or may beconsidered to comprise essentially the same composition) as the prioranalyzed portion if there is no chemical fractionation step or if therate of change of chemical composition caused by the fractionation ismuch slower than the rate at which the mass spectrometer system canrepeat steps 312-402.

Returning to step 310 from step 402 corresponds to mass isolating asecond, different predetermined m/z range or ranges of ion species instep 310 (as compared to the m/z range or ranges of ion species isolatedin the prior execution of step 310) and then repeating steps 312-402using the newly isolated ion species and their reaction products. Thisprocedure of repeating steps 310-402 is particularly useful if it may beassumed that the sample composition has not changed between successiveiterations of these steps. Under these circumstances, the repetition ofsteps 310-402 may provide additional information from the same samplecomposition. The sample may often be assumed to remain constant ornegligibly different if there is no chemical fractionation prior tosample introduction into the mass spectrometer or if the rate of changeof chemical composition caused by the fractionation is much slower thanthe rate at which the mass spectrometer system can repeat the steps312-402.

FIG. 3B schematically illustrates a flow diagram of a second exemplarymethod, method 370, in accordance with the present teachings. The steps302-314 of the method 370 (FIG. 3B) are identical to the similarlynumbered steps of the method 300 (FIG. 3A) and thus the description ofthese steps is not repeated here. The method 370 differs from the method300 only with regard to the steps following the generation of a massspectrum in step 314. According to the earlier-described method 300, themass spectrum of PTR product ions is assumed to be sufficient to detector quantify proteins and polypeptides of interest. However, in manycases, it may be necessary to perform tandem mass spectrometry(sometimes referred to as MS/MS or MS^(n)) after the generation of PTRreaction products in order to resolve remaining ambiguities in therecognition of specific protein or polypeptide molecules. In othercases, it may be necessary to perform a second stage of PTR as discussedfurther below. In cases in which fragmentation is required, the PTRreaction products may be considered to comprise a first generation ofreaction products which are then fragmented to form a second generationof product ions. The combination of a specific m/z ratio of afirst-generation reaction product with one or more specific m/z ratiosof fragment ions may, in many cases, allow identification of a specificprotein or polypeptide molecule associated with a given pathogen. Inmany instances the protein identified with a specific pathogen may alsobe found in other similar pathogens. In order to correctly identify asingle pathogen, method 370 (specifically tandem mass spectrometry) mayneed to be performed on as many proteins that are present in a given PTRfraction, or multiple PTR fractions of the same sample.

In step 316 of the method 370, a computational method is performed so asto automatically analyze the mass spectral data of PTR reaction productsobtained in step 314. The computational method attempts to identifycharge-state sequences of proteins or polypeptides. The results of thereal-time automatic computational analysis obtained in step 316 of themethod 370 may be later used as a basis for making an m/z selection inthe subsequent step 318, in which a subset of the PTR product ions,comprising a restricted range of m/z ratios, are selected and isolatedso as to be later fragmented in step 320. Preferably, the automaticidentification of charge-state sequences (step 316) is carried out by afast computational method, such as the computational methods that aredescribed in detail in the appendix to this document, that is optimizedfor such real-time data analysis.

Accordingly, steps 318-322 of method 370 (FIG. 3B) represent theapplication of the techniques of tandem mass spectrometry or selectedreaction monitoring (SRM) as applied to the ions formed by PTR. If theparticular employed mass spectrometry system permits, a portion of thePTR product ions may have already been stored (immediately after step312) in an ion storage apparatus of the mass spectrometer system. Insuch cases, the branching step 315 causes execution of step 317 a, inwhich the previously stored ions are retrieved for further processing.Otherwise, if the prior batch of PTR product ions was exhausted by themass analysis step (step 314), then, in accordance with the alternativestep 317 b, the steps 308-312 may need to be re-executed in order togenerate a new batch of such PTR product ions.

In step 318 of the method 370, certain of the PTR reaction-product ions(i.e., the first-generation product ions) within a particular m/z rangeor particular m/z ranges are mass isolated by ejecting ions whose m/zratios are not within the range or ranges of interest. The isolated ionsare subsequently fragmented in step 320. The particular chosen range orranges will generally be responsive to the details of a particularidentified charge-state sequence identified an immediately priorexecution of step 316 and the choice will generally be madeautomatically by computer. Thus, the choice of a particular m/z range orranges for isolation and fragmentation is an example of so-called“data-dependent analysis” (or “data-dependent acquisition”, etc.).

In most conventional MS/MS analyses, involving low-mass molecules of afew hundred to a few thousand Daltons, data-dependent fragmentationcomprises choosing the “top P number of the most abundant precursors”for tandem mass analysis based on the information of a preceding MS¹data acquisition, where the number P is either a constant or perhaps avariable input by a user. It has been found that this conventional formof data-dependent analysis does not perform well when used in theanalysis of multicomponent samples of biopolymer analytes. For example,FIG. 7C illustrates two charge state distributions, denoted by theenvelope 905 and the envelope 906, respectively. In this example, eachenvelope corresponds to a different respective analyte molecule species.Thus, the sets of lines encompassed by envelopes 905 and 906 may bereferred to as “molecular-species-correlative charge-statedistributions”. Considering the lines (individual m/z values) in FIG. 7Cto represent precursor ions, then if P=10, the conventionaldata-dependent fragmentation technique would choose the ten leftmostsolid vertical lines under the envelope 906 for fragmentation. Using theconventional technique, none of the dotted lines corresponding toenvelope 905 would be chosen. The conventional procedure would thusyield redundant information relating to the molecule speciescorresponding to envelope 906 and no information relating to themolecule species corresponding to envelope 905.

To overcome the shortcomings of conventional data-dependentfragmentation when applied to high-molecular-weight molecules, theinventors have developed the herein-used novel “top P uniqueanalyte-specific clusters” data-dependent technique so as to replace,for application to high-molecular-weight molecules, the previous “top Pnumber of the most abundant precursors” logic. Eachmolecular-species-correlative charge-state distribution is a set ofrelated mass spectral lines (m/z values) which are interpreted,according to the novel “top P unique analyte-specific clusters” logic,to all be generated from a single unique molecule. Eachmolecular-species-correlative charge-state distribution groups togethervarious charge states and isotopic clusters that are indicated to havebeen generated from a single molecule, prior to ionization. However, themolecular-species-correlative distribution excludes adducts, which areremoved prior to data analysis. According to the novel method,fragmentation is performed only on one (or possibly more) selectedrepresentatives of a given molecular-species-correlative charge statedistribution envelope thereby avoiding the redundancy noted aboveassociated with the conventional data-dependent fragmentation method.According to the novel “top P unique analyte-specific clusters” logic,after a representative m/z ratio (or ratios) has been chosen for a firstmolecular-species-correlative charge-state distribution, any furtherfragmentation is directed to a representative m/z ratio of the nextdetermined molecular-species-correlative charge-state distribution, andso on.

As previously described, the isolation performed in step 318 of themethod 370 may be accomplished by applying a supplemental AC voltageacross pairs of electrodes of an ion trap such that ions having m/zratios that are not within the range or ranges of interest are ejectedfrom the trap while those ions having m/z ratios that are within therange or ranges are retained within the trap. In some instances, the iontrap used for mass isolation may be identical to the mass analyzer usedto conduct the full-scan mass analysis in step 314.

The supplemental AC voltage applied to the ion trap used for massisolation may comprise a summation of superimposed frequencies such thations within two or more non-contiguous m/z ranges are simultaneouslyisolated. In the subsequent step 320, the mass-isolated first-generationproduct ions are fragmented by a suitable ion fragmentation technique,such as collision induced dissociation (CID). The fragmentation may beaccomplished by transferring the first-generation product ions (productions formed by PTR of original precursor ions), in known fashion, to adedicated fragmentation cell within which the transferred ions arefragmented so as to generate fragment ions, these fragment ionscomprising a second generation of reaction products. Optionally, aportion of the fragment product ions may be stored for possible futureadditional fragmentation in optional step 321.

In step 322 of the method 370 (FIG. 3B), the fragments generated in step320 are mass analyzed by a mass analyzer of the mass spectrometer. Ifthe second-generation product ions are produced within a fragmentationcell that is specifically dedicated for the purpose of fragmentation,the ions must be first transferred to the mass analyzer prior to theexecution of step 322. An ion trap mass analyzer may be employed toanalyze the second-generation product ions in step 322, in which casethe mass analyzer employed for step 322 may be identical to the massanalyzer employed to conduct the full-scan mass analysis of step 314.Alternatively, an accurate-mass analyzer capable of measuringmass-to-charge ratios to an accuracy of 10 ppm or better—such as anFT-ICR mass analyzer, a time-of-flight (TOF) mass analyzer or anOrbitrap™-type of electrostatic trap mass analyzer—may be employed forstep 322.

As is known, the correlation between the m/z value of a certain selectedion species subjected to fragmentation and the m/z value (or values) ofone or more fragment ion species produced by the fragmentation may besufficient to automatically determine (in step 402 b) the chemicalidentity of the selected ion species. In this case, the selected ionspecies is a PTR reaction-product species generated in step 312 that ismass-isolated in step 318. The identification of a small number (i.e.,3-10) of such proteins will generally be sufficient to uniquely identifya microorganism species (optional step 404 b). However, a single stageof fragmentation may be insufficient for performing a chemical speciesidentification. In such instances, the second generation product ionsmay be further fragmented so as to form a next generation of productions, indicated by the optional repeat (indicated with dashed lines)from step 322 back to step 318 in which a selected subset of thefragment product ions are isolated, according to their m/z values, andthe so-isolated fragment ions are further fragmented. More generally, asubset of the n^(th) generation of product ions may be selected forfurther fragmentation by any suitable ion fragmentation method such as,but not limited to, collision-induced fragmentation, higher-energycollisional dissociation, electron transfer dissociation, electroncapture dissociation, negative electron transfer dissociation,electron-detachment dissociation, surface-induced dissociation, orphotodissociation, whereby an (n+1)^(th) generation of product ions isformed. The results of the mass analysis step 322 may form the basis ofan automated decision as to whether or not each additional fragmentationis required and, if so, which m/z values correspond to the ion speciesto be fragmented.

In the above discussion, the optional repeat from step 322 back to step318 of the method 370 was described as for the purpose of furtherfragmenting previously-generated fragment ions. However, in someembodiments, the second or subsequent execution of step 318 may be forthe purpose of choosing a second, different ion species of the PTRreaction products (generated and possibly stored in step 312) forfragmentation, based on the automatic identification of charge statesequences previously performed in step 316. The possible need for asecond PTR step may be understood with reference now to FIG. 12C. As anexample, a first execution of the mass isolation step (step 318 ofmethod 370) may isolate an ion species corresponding to a mass spectralline belonging to charge state envelope A208, whereas a second executionof step 318 may isolate an ion species corresponding to one of the massspectral lines belonging to charge state envelope A206. As described inthe appendix to this document, each such set of mass spectral lines (theset belonging to envelope A208 or the set belonging to envelope A206).The novel “Top P Unique Analyte-Specific Clusters” workflow described inthe appendix is specifically adapted to recognize such clusterscorresponding to different respective potential analyte molecules.According to such a workflow procedure, the second execution of theisolation step (step 318) and the fragmentation step (step 320) of themethod 370 are performed so as to exclude isolation and fragmentation ofthe ion species corresponding to the envelope A208 (assuming one suchion species was isolated and fragmented during an earlier execution ofthese steps), even though they may be of greater intensity than the ionspecies corresponding to envelope A206. In this way, fragmentinformation about different potential analytes is obtained during eachiteration of the optional loop comprising steps 318-322.

The method 300 diagramed in FIG. 3A, which was discussed above, providesa relatively simple and straightforward method of sample analysis thatmay be applicable for samples of relatively low complexity as, forexample, when highly-resolved chromatographic separation (step 306) hasbeen performed prior to introduction of a chromatographic fraction intoa mass spectrometer (step 308). However, the simple method 300 may notbe appropriate for more complex samples and the analysis of such samplesmay present a number of challenges. Firstly, the proteins present in acomplex mixture have a wide range of molecular weights. Secondly, thelarge number of charge states that result from the presence of a largenumber of lysine, arginine, histidine residues may result in multipleoverlapping sets of peaks, each set of peak corresponding to a differentchemical species. Thirdly, if the mass analysis (step 314) is ofsufficiently high resolution, the presence of resolved peaks of anisotopic distribution for any given charge state can confound most dataprocessing algorithms. Finally, the distribution of available ions amongmultiple charge states and, possibly, among multiple isotopic statesnecessarily reduces the signal intensity of any resolved peak in themass analysis.

In order to address the above-noted challenges in the analysis ofcomplex samples, the method 380, for which a schematic flow diagram isillustrated in FIG. 3C, provides the opportunity for conducting multiplePTR stages. Under the earlier-described method 300, it is assumed thatthe mass spectrum obtained (in step 314) of the first-generation PTRreaction products (generated in step 312) exhibits sufficientimprovement in signal-to-noise ratio and sufficient reduction inisobaric interferences such that charge-state sequences may berecognized and that proteins or polypeptides may be identified. If suchimprovement in mass spectral quality remains inadequate for suchpurposes after a first PTR reaction event, then the additionalrefinement steps 327-330 of the method 380 (FIG. 3C) may be performed.Further, one or more of the PTR stages may utilize the known techniqueof “ion parking” in order to simplify the charge state distribution, asnoted in the previous paragraph. Ion parking is a technique wherebyspecific selected ion/ion reactions within an ion trap are inhibited. Inpractice, a resonance excitation waveform is applied across electrodepairs of an ion trap, ion guide or other ion storage device in anamplitude that is insufficient to cause ion ejection but sufficient toincrease the velocities of ions having selected m/z values. Thisexcitation process increases the relative velocity between the excitedions (cations, for purposes of the present discussion) and reagentanions and it is believed that this relative velocity increase causes areduction in the rates of reaction between the excited cations andreagent anions.

During the PTR process, the rate of reaction between cations and reagentanions varies as the square of the charge number of the various cationswith the anion charge on the reagent ions equal to −1. Thus, in theabsence of ion parking, the PTR process leads to a rapid reduction inthe number of highly charged cations. Over the course of the reaction,the distribution of charge states of cations derived from a singlemolecular species, M (a protein or polypeptide molecule having massm_(p)), shifts towards lower charge states. The population of each ionspecies having an intermediate charge state will first increase as themore-highly-charged precursor ions lose protons and then decrease aseach respective species loses more protons then it gains from thediminishing quantity of more-highly-charged cations. The ultimateresult, if the PTR reaction is allowed to proceed to completion, iscomplete neutralization of all such cations and total loss of all massspectrometric signal.

When the ion parking technique is applied during the PTR reaction, thenthe charge reduction process is essentially stopped at the charge state,z₁, corresponding to the particular mass-to-charge ratio (for example,m_(p)/z₁) of the ions which are resonantly excited by the applied ACwaveform. Those precursor cations derived from the molecular species, M,with initial charge states, z, such that z>z₁ will lose protons untiltheir charge states are reduced to z₁, after which further reaction andproton loss will be inhibitied. Those precursor cations derived from themolecular species, M, with initial charge states, z, such that z<z₁ willbe completely neutralized. Accordingly, after PTR reaction with ionparking, a significant portion of the original protonated molecular ions(i.e., precursor ions) of molecule M will be represented, in a massspectrum, by the single ionic species having charge state, z₁. This“concentration” of the molecule species, M, into a single charge statecan advantageously amplify the mass-spectrometric signal associated withthat species, thereby improving signal-to-noise ratio and reducing thelower limit of detection and, optionally, the lower limit ofquantification of the species. Further, many isotopic variants of ionsgenerated from molecule species, M, will have ink values outside of therange of values corresponding to the applied AC resonant excitationwaveform. Such isotopic variants will be neutralized so as to notinterfere with the mass spectrometric identification of ions ofinterest. Other isotopic variants comprise ink values that are withinthe range of values corresponding to the applied AC resonant excitationwaveform. The isotopic distribution pattern of such isotopically variantions will be greatly simplified relative to the isotopic distributionobserved in the original precursor ions because they will mostly relateto the single charge state, z₁ of ions generated from molecule, M.

Returning to the discussion of the method 380 outlined in FIG. 3C, it isto be noted that the steps 302-310 of the method 380 are identical tothe similarly numbered steps of the method 300 (FIG. 3A) and are notre-described here. Subsequently, in step 328, precursor ions aresubjected to PTR, optionally as modified by the ion parking technique.As previously noted, step 328 is executed by applying a supplemental ACexcitation waveform across a pair of electrodes of an ion trap withinwhich sample-derived cations are reacted with reagent anions for apredetermined time period. As described above, the employment of this“ion parking” procedure will concentrate the distribution of ionsderived from any particular protein or polypeptide into a particularrestricted range of m/z values. This will generally restrict the ionsderived from any particular protein or polypeptide into a particularcharge state, thereby simplifying a resulting mass spectrum andincreasing the intensity of any mass spectral peaks corresponding to theparticular protein or polypeptide. The particular range of m/z valuesinto which the ions are restricted may comprise ions of differentrespective charge states derived from different respective molecularspecies. In some embodiments, the applied AC waveform used to effect theion parking may comprise a summation of waveforms of differentrespective frequencies such that the summed waveform causes the PTRreaction to yield a final population of PTR product ions correspondingto two or more non-contiguous m/z ranges.

In the subsequent step 330, the population of PTR product ions producedin step 328 is mass analyzed by a mass analyzer and, in step 331, anautomatic computation may be performed on the data produced in the massanalysis so as to automatically identify any charge-state sequences thatmay be represented in the data, where each such charge state sequencecorresponds to a different potential protein or polypeptide analyte.Prior to the mass analysis of step 330, a portion of the PTR productions may be stored (step 329) in preparation for possible subsequent PTRreaction. Depending upon the results of the automatic identification ofcharge state sequences (step 331), an automatic decision may be made tosubject the PTR product ions to such further PTR reaction, as indicatedby the dashed line optional pathways shown in FIG. 3C. The decision mayalso be made, based on the results of the automatic identification ofcharge state sequences, to only subject a selected subset of the PTRproduct ions to subsequent PTR reaction. In such cases, step 327 isexecuted. Thus, according to some embodiments, the steps 327-331 maycomprise an iterated loop wherein, at each iteration of the loop, adifferent respective ion species, corresponding to a differentrespective protein or polypeptide is selected and isolated for furtherpurification by the PTR process. The ion species that are selected inthis fashion may be determined in step 331 from in accordance with thenovel “Top P Unique Analyte-Specific Clusters” workflow described in theappendix to this document.

If the mass analyzer employed in step 330 is of a type that detectsimage currents produced by cyclic ion motion within an ion trap or otherion storage device—such as an FT-ICR mass analyzer or an Orbitrap™ massanalyzer—then the PTR reaction steps may advantageously reduce collisionprofiles of targeted protein or polypeptide molecules such that thesemolecules remain stable in the trap for a sufficient length of time togenerate high-quality mass spectra. After a sufficient number of PTRreaction steps, the chemical identity of the protein or polypeptide maythen be rapidly discerned (in step 402 c) by matching to databases ofknown molecular masses. The identification of a small number of (3-10)of proteins will generally be sufficient to uniquely identify amicroorganism species (optional step 404 c). Identification can also beaccomplished via the use of classifiers applied to the PTR data asdiscussed previously that includes but is not limited to Bayesian,logistic regression or decision tree based approaches.

FIGS. 3D-3E illustrate, in flow diagram form, another method, method390, in accordance with the present teachings. The steps 302-331 of themethod 390 are shown in FIG. 3D and are identical to thepreviously-discussed similarly-numbered steps of the method 380 (FIG.3C); thus, these steps are not re-described here. Instead of proceedingto the identification step 402 d directly from step 330 (as in themethod 380 of FIG. 3C), execution of the method 390 (FIGS. 3D-3E)proceeds from step 330 to a mass selection and isolation step 332. Instep 332, a subset of the PTR product ions—generated by one or moreapplications of the PTR procedure—are isolated according to selected m/zratios. Decisions regarding the specific m/z ratios to be isolatedduring this step may be automatically performed based on the massspectrometric results obtained in step 330 or, with respect to anoptional subsequent mass isolation and fragmentation (see optionalrepeat branch of FIG. 3E), based on the results of a mass analysis ofthe fragment ions themselves (step 338). The steps 332-338 illustratedin FIG. 3E represent an ion fragmentation procedure which may beiterated (see optional repeat branch) so as to produce multiplegenerations of fragmentation product ions. These steps 332-338 aresimilar to the steps 318-322 of the method 370 illustrated in FIG. 3Band are thus not discussed in detail.

After execution of the fragmentation and mass analysis steps, thepeptide identification step 402 d of the method 390 (FIG. 3E) isexecuted. Whereas the identification step 402 a of the method 300 (FIG.3A) makes use only of the m/z ratios (or molecular weights) of ionspecies comprising protonated or multiply-protonated analyte molecules,the identification step 402 d of the method 390 also takes into accountthe m/z ratios of the fragments—possibly of multiple generations—ofthese ion species. Thus, in the case of complex mixtures of proteins orpolypeptides, a greater confidence may be associated with the results ofthe identifications made using the method 390. Control of theexperiments may be performed in real time according to some embodimentsby making use of real-time data deconvolution as noted above. Theidentification of a small number of (3-10) of proteins species in step402 d will generally be sufficient to uniquely identify a microorganismspecies in step 404 d.

FIG. 3F diagrammatically illustrates, in flow diagram form, anothermethod, method 395, in accordance with the present teachings. Most ofthe steps in the method 395 (FIG. 3F) are similar to similarly numberedsteps in the method 370 (FIG. 3B) and these steps are not re-describedin detail. Similarly to the method 370, the method 395 includes a step(step 312) of subjecting original precursor ions to PTR charge reductionfollowed by steps (steps 318 and 320) of isolating selected PTR production species and subjecting the isolated ion species to fragmentation soas to form fragment product ion species. The method 395 differs frommethod 370 through the provision of an additional step, step 340, ofsubjecting the fragment ions to PTR charge reduction. Since the variousPTR product ion species generated from the original precursor ions maybe multiply-charged and may be distributed among species with variousdegrees of protonation, the fragment ions formed from them maythemselves be distributed among multiple protonation states. The PTRcharge reduction of the fragment ion species in step 340 can simplifythe charge state distributions of the fragment ions prior to their massanalysis in step 341. Optionally, any of the PTR steps (step 312 andstep 340) may employ ion parking. An optional repeat from step 341 backto step 318 so as to repeat the steps 318-341 may be performed for thepurpose of choosing a second, different ion species of the PTR reactionproducts (generated and possibly stored in step 312) for fragmentation,based on the automatic identification of charge state sequencespreviously performed in step 316. As previously discussed with respectto the method 370 (FIG. 3B), the second, different ion species maybelong to a set of lines of a second charge state envelope that isdifferent from the charge state envelope to which a prior isolated andfragmented ion species belongs (see also FIG. 12C). The novel “Top PUnique Analyte-Specific Clusters” workflow described in the appendix isspecifically adapted to recognize such clusters corresponding todifferent respective potential analyte molecules. In this way, fragmentinformation about different potential analytes is obtained during eachiteration of the optional loop comprising steps 318-341 of the method395.

EXAMPLE A

FIGS. 4A and 4B provide an example of mass spectroscopic signalenhancement provided by a single PTR reaction step (e.g., as in themethod 300 shown in FIG. 3A). In a first application (FIGS. 4A, 4B), anextract from the pathogen E. coli was analyzed via direct infusion; themass spectrum of the first-generation electrospray-generated ions isshown in FIG. 4A. As expected, there are many proteins present thatoverlap at various m/z values leading to the presence of a broadspectral region between approximately m/z=780 and m/z=1420 within whichmany ions are detected but with very little usable information in termsof discernible protein charge state distributions. Next, an m/z “window”of the first-generation ions of width 2 Th and centered at m/z=750 wasisolated and the resulting isolated ion population was subjected to PTRreaction. The m/z position 412 a shown in FIG. 4A indicates the centerposition of the isolation window.

FIG. 4B shows a mass spectrum of the PTR reaction products of precursorions of the E. coli extract. The PTR reactions were carried out withreagent anions derived from 3 ppm of sulfur hexafluoride (SF₆) in anitrogen gas stream delivered to a glow discharge reagent ion sourcecontained within the ion optics of a mass spectrometer of the samegeneral configuration as illustrated in FIG. 2. As with most PTRproduct-ion spectra, the mass spectrum shown in FIG. 4B exhibits arelatively intense isolated peak at the position (indicated as position412 b) of the original first-generation-ion isolation window. Such peaksat the position of the isolation window generally indicate the presenceof residual singly-charged first-generation ions—generally not ofinterest—that fortuitously occur at the position of the isolation. Otherpeaks in the spectrum of FIG. 4B represent product ions generated fromthe PTR reaction. These product ions generally comprise overlapping setsof related ions, each set corresponding to ions comprising adistribution of charge states from an original multiply-chargedprecursor ion within the original isolation window. One such potentialcharge-state distribution pattern is approximately indicated by theenvelope 413. The results shown in FIGS. 4A and 4B show that the PTRreaction process generally significantly simplifies the spectrum andreduces background interference. Nonetheless, since many protein-derivedor peptide-derived precursor ions may be present in the originalisolation window, the charge-state distribution patterns may overlap.Mathematical decomposition (sometimes referred to as “deconvolution”)may be required to recognize the individual patterns.

EXAMPLE B

FIGS. 5A and 5B illustrate an example of analysis of an E. Coli extractthat is performed by a procedure that includes two stages of PTRreaction (for example, see steps 327, 328, 329 and 330 of method 380 inFIG. 3C). FIG. 5A illustrates a PTR product ion spectrum generatedisolated first-generation precursor ions from within a 5 Th mass windowcentered at m/z=1200, indicated by position 711 in FIG. 5A. In thisinstance, the initial PTR spectrum does not include peaks that aresufficiently well resolved to enable identification of any proteins inthe sample. Therefore, a subset of the first-generation PTR product ionswere isolated for a second stage of PTR from within a 5 Th mass windowcentered at m/z=1320, indicated by position 712 a in FIG. 5A andposition 712 b in FIG. 5B. The second-generation PTR product ions, whichoccur at m/z ratios greater than 1320 in FIG. 5B show clear charge-statedistribution patterns that may be successfully used for identificationof proteins in the sample.

FIGS. 6A-6G illustrate an example of analysis of an E. coli extract thatis performed by a procedure that includes a first stage of product ionformation by PTR reaction followed by subsequent stages of CID of thePTR reaction product ions (for example, see steps 312 through 322 method370 in FIG. 3B). FIG. 6A illustrates a PTR product ion spectrumgenerated isolated first-generation precursor ions from within a 5 Thmass window centered at m/z=640, indicated by position 811 in FIG. 6A.The PTR product ions occur at m/z ratios greater than that indicated byposition 811 in FIG. 6A. The three most intense PTR product ions,located at m/z ratios of 833 , 926 and 917 and indicated by massspectral peaks 813, 814 and 815, respectively, in FIG. 6A, were thenindividually isolated and separately subjected to collision-induceddissociation so as to produce three sets of second-generation productions. FIGS. 6B and 6C respectively depict the isolated PTR product ionat m/z=833 and the second generation product ions (fragment ions)generated by CID of the isolated PTR product ion Likewise, FIGS. 6D and6E respectively depict the isolated PTR product ion at m/z=926 and thesecond generation product ions generated by CID of the isolated PTRproduct ion at m/z=926. Likewise, FIGS. 6F and 6G respectively depictthe isolated PTR product ion at m/z=917 and the second generationproduct ions generated by CID of the isolated PTR product ion atm/z=917.

EXAMPLE C

As should be evident from the previous discussions, positive ionelectrospray ionization of any protein or polypeptide molecule willproduce a plurality of ions comprising different respective chargestates (i.e., number of charges) as a result of different degrees ofprotonation of the original molecule. Charge states of +50 or more orpossible and each charge state will be represented by multiple massspectral lines representing different degrees of natural isotopicsubstitution. A further complication arises from the fact that for mostnatural biological samples, numerous different proteins of polypeptidemolecules may be represented in a mass spectrum. A yet furthercomplication arises from the fact that many other molecules—notnecessarily of interest—may be present in a sample.

In many basic-research-oriented studies, the above-noted complicatingfactors of multiple analytes and multiple interfering species may bepartially or wholly resolved by performing chromatic separation prior tointroducing each separated compound individually into a massspectrometer. However, clinical analyses may often be performed undertight time constraints that do not allow for traditional time-consumingchromatographic separation. The clinical time constraints may only allowfor an incomplete or partial separation using either solid-phaseextraction (SPE), size-exclusion chromatography, or the method of FastPartial Chromatographic Separation (FPCS) described above. Thus, whensuch partial separation procedures are employed, the mass spectralsignature of any particular protein or polypeptide may be spread outover a wide mass-to-charge ratio and may be complexly overlapped withthe mass spectral signatures of other compounds. Since the availablecharge, as provided by an electrospray apparatus, will be spread outover many different types of ions, most of the observed mass spectrallines will coexist with and possibly be hidden within a general denselypopulated and low-intensity or ill-defined spectral “background”indicated schematically by spectral envelope 902 in FIGS. 7A-7B.

The inventors have realized that the mass spectral signature of anyparticular protein, polypeptide or other biologically relevanthigh-molecular-weight analyte may be hypothetically amplified bysimultaneously isolating multiple charge states of the same originalmolecule and then reacting the assemblage of multiple charge states withPTR reagent ions so as to simultaneously reduce the assemblage to asmall number of charge states distributed over a few charge-statevalues, these charge-state values being reduced relative to the originalcharge states. This concept is illustrated by the vertical boxes 904a-904 g shown overlaid over the general charge-state envelope 902 inFIG. 7A. Each such vertical box represents a particular precursor ionspecies and represents a small range of m/z values chosen to correspondto a particular charge state (and possibly including a few isotopicvariants) of a particular analyte. Hypothetically, if all ions outsideof the ranges corresponding to the vertical boxes could be excluded andonly the ions from within the indicated ranges mixed together, thensubsequent PTR would essentially provide a summation of the signals fromthe various original plurality of charge states. The use of suchmulti-species isolation of a plurality of precursor ion species canincrease the sensitivity of the analysis up to N-fold, where N is thenumber of m/z ranges selected and simultaneously isolated.

Such multiple-species isolation is fairly easy to achieve when isolationis performed in a linear ion trap (such as the low-pressure linear trapcell 217 b illustrated in FIG. 2), because resonance-excitationwaveforms, which are used to eject unwanted ions, may be constructedwith multiple notches. Each such notch corresponds to a differentrespective m/z window within which ions will not be ejected (and thusisolated). Thus, the co-isolating of a plurality ofelectrospray-generated (first-generation) precursor ion species may beperformed, in some embodiments, by simultaneously isolating all of theplurality of precursor ion species. One way of doing this is by applyinga broadband resonance ejection frequency waveform to an ion trap intowhich ions received from an electrospray source have been introduced,wherein the waveform comprises multiple summed sinusoidal frequencycomponents, wherein included frequency components corresponding to them/z ranges of ions that one desires to eject from the trap and excludedfrequency components correspond to the m/z range of ions that onedesires to retain within the trap. In this procedure, the omittedfrequencies define one or more frequency notches in the ejectionfrequency waveform. The frequency components may be calculated by firstchoosing a desired multi-notch waveform and then calculating an inverseFourier Transform of the desired waveform.

Alternatively, the co-isolating of the plurality of precursor ionspecies may be performed by isolating individual precursor ion speciesin a conventional sense, one ion species at a time using a respectivesingle-notch waveform applied to an ion trap. The individually isolatedprecursor ion species may be transferred, one at a time, to an ionstorage component (such as the multipole ion guide 214 illustrated inFIG. 2) in which the various selected and isolated ion species areaccumulated over time. As a yet-further alternative, the co-isolating ofthe plurality of precursor ion species may be performed by passing aplurality of ions received from an electrospray source through aquadrupole mass filter while the bandpass of the quadrupole mass filteris sequentially tuned to preferentially transmit, in turn, each m/zrange corresponding to a particular precursor ion species. The filteredions that pass through the quadrupole mass filter are then passed intoan ion storage component that accumulates the ions from all thepreferentially transmitted m/z ranges. For example, in the massspectrometer 150 a illustrated in FIG. 2, the quadrupole mass filter 208may perform the sequence of filtering steps and the ions of eachtransmitted m/z range may be transmitted into and accumulated within themultipole ion guide 214. The accumulated precursor ion species may thenbe transferred back to the low-pressure cell 217 b for PTR reaction.

The above-described procedure employing simultaneous multi-speciesisolation assumes that appropriate isolation ranges 904 a-904 g a prioriknown. Such knowledge about the correct isolation ranges to employ maybe available in certain instances of targeted analysis, when theidentity of (and other information pertaining to) an analyte that is tobe searched for is already known and the purpose of the analysis is todetermine the presence or absence of the analyte or to determine thequantity or concentration of the analyte. However, the above assumptionmay be invalid in the case of survey analyses, in which the identitiesof analytes may not be known in advance. In such latter cases, aninitial random survey may be performed by isolating a random mass range903 of the first-generation ions, as schematically depicted in FIG. 7B,and then reacting the isolated ions with a PTR reagent anion. Aspreviously illustrated in FIGS. 4A and 4B, such a procedure can provideresolved, interpretable mass spectral lines relating to charge statedistributions of one or more analytes. In many instances, a set ofrelated lines may be recognized with by the mutual consistency of theirm/z values with Eq. 1 for a certain sequence of consecutive integers, z.The degree of consistency of the line positions may be performedautomatically, by computer analysis, such that overlapping sets of suchrelated lines may be mathematically decomposed and recognized.

As an example of the above type of analysis, mathematical decompositionof the PTR product ion lines generated by isolation and reaction ofprecursor ions within m/z range 903 may lead to recognition of twooverlapping sets of lines, depicted by envelope 905 and envelope 906, asillustrated in FIG. 7C. With the information provided by this initialsurvey procedure, an appropriate and consistent set of m/z values may bechosen, which may be employed in a subsequent simultaneous multi-speciesisolation and reaction procedure. For example, the m/z values of certainresolved instances of the lines under envelope 905 may be chosen,perhaps automatically. Subsequent multi-species isolation and PTRreaction of precursor ions corresponding to these chosen m/z values willthen provide an amplified spectrum that may be employed to determine aquantity or concentration of the particular molecule represented by theenvelope 905. This procedure may later be repeated using the associatedwith envelope 906 so as to determine a quantity or concentration ofanother molecule. The determined quantities or concentrations may not beaccurate, in an absolute sense, but the ratios of the determinedquantities or concentrations may provide useful information relating torelative quantities or concentrations. This entire procedure outlinedabove may be repeated multiple times using different randomly chosen m/zranges 903, thereby providing determinations of relative quantities orconcentrations of several compounds. As stated previously, control ofsuch experiments can be accomplished in a data-dependent fashionutilizing the results of real-time spectral deconvolution.

FIG. 8 provides a general flow diagram of an exemplary method, method397, of survey analysis using the above-outlined PTR signalamplification by reaction of PTR reagent ions with first-generation ionsfrom multiple non-contiguous m/z ranges. Steps 302, 304, 306, 308, 309,312, 314, 316, 402 and 404 of the method 397 are identical to thesimilarly numbered steps of the method 300 illustrated in FIG. 3A andare thus not re-described here. Also, the new step 311 is similar to thepreviously described step 310 of method 300, except that step 311 refersonly to mass isolation of a random m/z range (such as the range 903depicted in FIG. 7C) of first-generation ions, instead of to a “randomor predetermined m/z range or ranges” as described for the prior method300. After the initial survey PTR reaction (step 312) and identificationof charge-state sequences (step 316), the latter of which is preferablyperformed by the computational methods described in the appendix to thisdocument, the step 323 is executed, in which multiple non-contiguous m/zranges of the first-generation ions are isolated and accumulated,wherein the non-contiguous m/z ranges correspond to an identified chargestate sequence. The first-generation ions may be obtained from apreviously stored batch of such ions (prior step 319 a) or,alternatively (prior step 319 b), the sample introduction andelectrospray ion generation step may need to be repeated.

After the isolation and accumulation of multiple non-contiguous m/zranges of the first-generation ions (step 323), the accumulated ions arereacted with PTR reagent ions (step 324). The resulting amplifiedspectra will generally be of high quality thereby facilitating thederivation (step 325) of, for example, an accurate molecular weight ofthe molecule corresponding the multiple non-contiguous m/z ranges or anaccurate quantity, concentration, or relative abundance of suchmolecule. If an immediately prior execution of step 316 identified morethan one set of related m/z ratios, then step 319 a or 319 b and steps323-325 may be executed again (following the leftmost “Y” branch of step326) using a new set of non-contiguous m/z ranges that correspond to adifferent identified charge state sequence. If a search for possibleadditional analytes is to be continued, then execution may return tostep 311 (following the rightmost “Y” branch of step 326) at which adifferent random m/z range is chosen.

EXAMPLE D

According to another method for reduction of sample complexity utilizingproton transfer reactions in accordance with the present teachings, massspectrometric analysis employing PTR can be coupled directly withchromatography in order to simplify and detect additional proteins thatwould otherwise be missed. In this embodiment, a full scan mass spectrumis taken and the protein molecular weights are calculated using areal-time deconvolution program. Next, an isolation window is chosen ofa defined width and the subset of m/z values in the window are subjectedto PTR reactions.

For example, FIG. 9A shows a full scan mass spectrum of first-generationions generated from eluate at a retention time of 10 min. and 30 s.during the course of a ten-minute gradient reverse-phase liquidchromatography separation of an E. coli extract. As indicated by thebraces in FIG. 9A, this full-scan mass spectrum exhibits the distinctspectral signatures of two proteins having approximate molecular weightsof 35.1 and 31.1 kDa respectively. For the next step, a population ofions having m/z values within an m/z isolation window 510 of 10 Th widthand centered at 750 Th were isolated. The isolated ion population wasthen subjected to PTR reactions with the anionic reagent sulfurhexafluoride for 10 ms. The resulting product ion mass spectrum, shownin FIG. 9B, exhibits the mass spectral signatures of two additionalproteins not seen in the full-scan mass spectrum having molecularweights of 11220.07 Da and 24599.56 Da. In addition, the 35.1 kDaprotein component previously observed in the full-scan mass spectrumalso exhibits a spectral signature in the PTR product ion spectrum whichincludes a line, outlined in box 520, corresponding to a +47 chargestate at a nominal m/z value of 749. The line at 749 Th representscharge reduction of even-more-highly-charged states of the 35.1 kDaprotein. The proteins observed at 11.2 and 24.6 kDa would not otherwisebe identified in the absence of the PTR step in this example of areverse-phase chromatographic run as a result of complex spectraloverlap and interfering noise from an abundance of singly-chargedbackground ions.

FIGS. 10A and 10B show the results of a similar chromatography/MSexperiment obtained from eluate at a retention time of 42 min. and 30 s.from a sixty-minute gradient elution run. As shown in FIG. 10A, a highbackground at this elution time causes difficulty in identifying analytepeaks in the full-scan spectrum. However, the PTR product ion spectrumplotted in FIG. 10B is much more amenable to interpretation and massspectral deconvolution. The PTR product ion spectrum exhibits the massspectral signatures of three distinct proteins—specifically havingmolecular weights 11165.92 Da, 13480.28 Da and 18727.23 Da—that wouldnot otherwise be observed. In this instance, the PTR product ions weregenerated from isolated precursor ions generated from the mass spectralwindow, indicated by box 610 in FIG. 10A, of 10 Th width centered at m/z750. By performing this type of analysis upon eluates that elute atvarious different retention times during the course of a singleexperiment, a sufficient number of sample peptides may be recognized soas to enable identification of a microorganism to the species,subspecies, or strain level. As also indicated by the results shown inFIGS. 9A-9B, if there is m/z overlap of protein ions from the full massspectrum within the isolation window, then the protein will also be seenin the PTR product ion mass spectrum.

Interestingly, the full scan mass spectrum and PTR product-ion massspectrum can provide complementary information, as illustrated in FIGS.11A and 11B which represent mass spectral results obtained from eluateeluting at a retention time of 18 min. and 9 s. over the course of athirty-minute chromatographic separation. In this example, the full-scanmass spectrum (FIG. 11A) exhibits a strong mass spectral signature ofessentially a single protein having a molecular weight of 9534.3 Da,However, when a PTR product ion spectrum is generated from ions isolatedwithin a 10 Th wide window centered around m/z 750 Th (box 530), themass spectral signature comprises a strong signal from a protein havinga molecular weight of 14965.5 Da (best represented by the peak 535 ofthe +12 charge state at approximately 1247 Th) along with five otherminor proteins having molecular weights of 12669.8 Da, 14150.0 Da,14236.1 Da, 14965.5 Da, and 15117.5 Da. FIG. 11C is a full-scan massspectrum obtained from eluate eluting during the same chromatographicseparation at a retention time of 22 min. and 27 s. The spectrumincludes peaks indicating the presence of a protein having a molecularweight of 24961.3 Da. Upon PTR reaction of ions isolated within theisolation window 540, the PTR product ion spectrum shown in FIG. 11D wasobtained. The mass spectral signature in the PTR product ion spectrumincludes a relatively strong signal from a protein having a molecularweight of 28461.5 Da (best represented by the peak 545 of the +22 chargestate at approximately 1294 Th) as well as two other proteins havingmolecular weights of 18590.5 Da and 20168.0 Da. Thus, from just the dataat these two retention times, it is possible to detect the presence andthe molecular weights of eleven different proteins.

ADDITIONAL EXAMPLES

The following paragraphs list additional specific examples of variousspecific embodiments in accordance with the present teachings.

Example 1

A method for identifying the presence or absence of a protein orpolypeptide analyte compound within a liquid sample comprising a mixtureof compounds that includes a plurality of protein compounds or aplurality of polypeptide compounds or pluralities of both protein andpolypeptide compounds, the method comprising:

-   -   (a) introducing a portion of the liquid sample into an        electrospray ionization source of a mass spectrometer;    -   (b) forming positively charged ions of the mixture of compounds        of the portion of the liquid sample by electrospray ionization,        the positively charged ions comprising a plurality of ion        species;    -   (c) isolating a first subset of the ion species comprising a        first mass-to-charge (m/z) ratio range that includes an m/z        ratio of a particular predetermined multiply-protonated        molecular species of the analyte compound;    -   (d) generating a plurality of first-generation product ion        species from the isolated first subset of ion species by causing        the isolated first subset of ion species to be reacted, for a        predetermined time duration, with reagent anions that, upon        reaction, extract protons from each of one or more ion species        that comprises a protonated molecular species of a protein or        polypeptide compound;    -   (e) generating a mass spectrum, using a mass analyzer, of either        the first-generation product ion species or of second-generation        product ion species generated from the first-generation product        ion species;    -   (f) conducting a search of the mass spectrum of either the        first-generation or the second-generation product ion species        for a set of one or more m/z ratios that are diagnostic of the        protein or polypeptide analyte compound; and    -   (g) identifying the presence of the analyte compound within the        sample if the set of one or more m/z ratios is identified in the        mass spectrum.

Example 2

A method as recited in Example 1, further comprising repeating the steps(a) through (e) a second time, wherein the steps (f) and (g) areperformed during or prior to the second performing of the steps (a)through (e).

Example 3

A method as recited in Example 1, further comprising repeatedlyperforming steps (a) through (g) a plurality of times, wherein eachrepetition of step (a) comprises introducing, into the electrosprayionization source, an eluate from a chromatographic column correspondingto a respective retention time.

Example 4

A method as recited in Example 1, wherein the step (f) comprisesconducting a search of the mass spectrum of the first-generation production species for a series of m/z ratios that correspond to a sequence ofmultiply-protonated ion species of the analyte compound that areprogressively charge-reduced with respect to the charge state of theparticular predetermined multiply-protonated molecular species.

Example 5

A method as recited in Example 1, wherein:

-   -   the step (c) comprises further isolating a second subset of the        ion species comprising a second m/z ratio range that includes an        m/z ratio of a particular predetermined multiply-protonated        molecular species of a second protein or polypeptide analyte        compound;    -   the step (f) comprises conducting an additional search of the        mass spectrum of either the first-generation or the        second-generation product ion species for a second set of one or        more m/z ratios that are diagnostic of the second protein or        polypeptide analyte compound; and    -   the step (g) comprises identifying the presence of the second        analyte compound within the sample if the second set of m/z        ratios is identified in the mass spectrum.

Example 6

A method as recited in Example 5, wherein the first m/z ratio range isidentical to the second m/z ratio range.

Example 7

A method as recited in Example 5, wherein the step (c) comprisessimultaneously isolating the first subset of the ion species comprisingthe first m/z ratio and the second subset of the ion species comprisingthe second m/z ratio range such that the first and second m/z ratioranges are non-contiguous.

Example 8

A method as recited in Example 1, wherein the step (d) of generating aplurality of first-generation product ion species comprises causing theisolated first subset of ion species and reagent anions to be reactedfor a time duration that causes the product ion species to be stableagainst decomposition during the subsequent generation of the massspectrum in step (e).

Example 9

A method as recited in Example 8, wherein the step (e) comprisesgenerating a mass spectrum of the first-generation product ion speciesusing a mass analyzer that generates the mass spectrum by detectingimage currents caused by motions of the ions of the product ion specieswithin an ion trap.

Example 10

A method as recited in Example 1, wherein the step (d) of generating aplurality of first-generation product ion species includes applying asupplemental AC voltage across electrodes of an ion trap within whichthe isolated first subset of ion species are reacted with reagentanions, wherein a frequency of the supplemental AC voltage is such thation-ion reaction between the reagent anions and selectedfirst-generation product ion species is inhibited.

Example 11

A method as recited in Example 10, wherein the frequency of thesupplemental AC voltage is such that, subsequent to the execution ofstep (d), product ions formed from the analyte compound existsubstantially as a single ion species having a particular charge state.

Example 12

A method as recited in Example 11, wherein:

-   -   the step (e) comprises generating a mass spectrum of the        first-generation product ion species; and    -   wherein the mass of the single ion species is greater than        20,000 Da and the charge state of the single ion species is        sufficiently great such that ions of the single ion species may        be detected, during the generation of the mass spectrum, by        either a quadrupole mass analyzer, a Fourier transform ion        cyclotron resonance mass spectrometer or an electrostatic trap        mass analyzer.

Example 13

A method as recited in Example 1, wherein the step (e) of generating amass spectrum comprises generating a mass spectrum of second-generationproduct ion species, wherein the second-generation product ion speciesare generated by the steps of:

-   -   isolating a subset of the first-generation product ion species        comprising a particular product-ion m/z ratio range; and    -   fragmenting the isolated subset of the first-generation product        ion species so as to form fragment ion species, wherein the        fragment ion species comprise the second-generation product ion        species.

Example 14

A method as recited in Example 1, wherein the step (e) of generating amass spectrum comprises generating a mass spectrum of second-generationproduct ion species, wherein the second-generation product ion speciesare generated by:

-   -   causing the first-generation product ion species to be reacted,        for a second predetermined time duration, with the reagent        anions, wherein products of reaction between the        first-generation product ion species and the reagent anions        comprise the second-generation product ion species.

Example 15

A method as recited in Example 14, wherein a supplemental AC voltage isapplied across electrodes of an ion trap within which thefirst-generation product ion species are reacted with the reagentanions, wherein a frequency of the supplemental AC voltage is such thation-ion reaction between the reagent anions and selected product ionspecies is inhibited.

Example 16

A method as recited in any one of Examples 1-15, further comprisinggenerating the liquid sample comprising the mixture of compounds by aprocedure comprising:

-   -   (i) culturing microorganisms or cells;    -   (ii) lysing the cultured microorganisms or cells; and    -   (iii) extracting proteins from the lysate of cultured        microorganisms or cells.

Example 17

A method as recited in Example 16, wherein the step (iii) of extractingthe liquid sample from the lysate includes passing the lysate through asolid-phase-extraction apparatus.

Example 18

A method of identifying the presence or absence of a microorganism typein a sample, comprising:

-   -   (i) identifying a list of analyte compounds whose simultaneous        presence in the sample is diagnostic of the presence of the        microorganism type in the sample, said list of analyte compounds        comprising protein compounds, polypeptide compounds or both        protein and polypeptide compounds;    -   (ii) extracting, from the sample, a liquid solution comprising a        mixture of sample-derived proteins and polypeptides;    -   (iii) for each respective analyte compound in the list,        performing the steps of:        -   (a) introducing a portion of the liquid solution into an            electrospray ionization source of a mass spectrometer;        -   (b) forming positively charged ions of the mixture of            compounds of the portion of the liquid solution by            electrospray ionization, the positively charged ions            comprising a plurality of ion species;        -   (c) isolating a first subset of the ion species comprising a            first mass-to-charge (m/z) ratio range that includes an m/z            ratio of a particular predetermined multiply-protonated            molecular species of the respective analyte compound;        -   (d) generating a plurality of first-generation product ion            species from the isolated first subset of ion species by            causing the isolated first subset of ion species to be            reacted, for a predetermined time duration, with reagent            anions that, upon reaction, extract protons from each of one            or more ion species that comprises a protonated molecular            species of a protein or polypeptide compound;        -   (e) generating a mass spectrum, using a mass analyzer, of            either the first-generation product ion species or of            second-generation product ion species generated from the            first-generation product ion species;        -   (f) conducting a search of the mass spectrum of either the            first-generation or the second-generation product ion            species for a set of one or more m/z ratios that are            diagnostic of the respective analyte compound; and        -   (g) identifying the presence of the respective analyte            compound within the liquid solution if the set of one or            more m/z ratios is identified in the mass spectrum; and    -   (iv) identifying the presence of the microorganism type within        the sample if the presence of each and every analyte compound of        the list of analyte compounds is identified within the liquid        solution.

Example 19

A method of identifying the presence or absence of a microorganism typein a sample, comprising:

-   -   (i) identifying a list of analyte compounds whose simultaneous        presence in the sample is diagnostic of the presence of the        microorganism type in the sample, said list of analyte compounds        comprising protein compounds, polypeptide compounds or both        protein and polypeptide compounds;    -   (ii) extracting, from the sample, a liquid solution comprising a        mixture of sample-derived proteins and polypeptides;    -   (iii) introducing at least a first portion of the liquid        solution into an ionization source of a mass spectrometer;    -   (iv) generating, from the at least first portion of the liquid        solution at the ionization source, positively charged ions of        the mixture of compounds, the positively charged ions comprising        a plurality of ion species;    -   (v) isolating at least a first subset of the plurality of ion        species, each isolated subset of the at least a first isolated        subset comprising a respective mass-to-charge (m/z) ratio range;    -   (vi) generating a plurality of first-generation product ion        species from each isolated subset of ion species by causing each        said isolated subset of ion species to be reacted, for a        predetermined time duration, with reagent anions that, upon        reaction, extract protons from each of one or more ion species        of said isolated subset of ion species that comprises a        protonated molecular species of a protein or polypeptide        compound;    -   (vii) generating at least one mass spectrum, using a mass        analyzer of the mass spectrometer, of either first-generation        product ion species or second-generation product ion species        generated by further reaction of the first-generation product        ion species;    -   (viii) for each respective analyte compound in the list,        performing the steps of:        -   (a) conducting a search of the at least one mass spectrum of            either the first-generation or the second-generation product            ion species for a set of one or more m/z ratios that are            diagnostic of the respective analyte compound; and        -   (b) identifying the presence of the respective analyte            compound within the liquid solution if the set of one or            more m/z ratios is identified in the mass spectrum; and    -   (ix) identifying the presence of the microorganism type within        the sample if the presence of each and every analyte compound of        the list of analyte compounds is identified within the liquid        solution.

Example 20

A method as recited in Example 19, wherein a performing of the steps (a)and (b) is performed concurrently with the performing of one or more ofthe steps (iii) through (vii).

Example 21

A method as recited in Example 19, wherein the microorganism type isdefined as a particular genus of bacteria and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular genus of bacteria to enable identificationof the presence or absence of the particular genus of bacteria in thesample.

Example 22

A method as recited in Example 19, wherein the microorganism type isdefined as a particular species of bacteria and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular species of bacteria to enableidentification of the presence or absence of the particular species ofbacteria in the sample.

Example 23

A method as recited in Example 19, wherein the microorganism type isdefined as a particular sub-species of bacteria and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular sub-species of bacteria to enableidentification of the presence or absence of the particular sub-speciesof bacteria in the sample.

Example 24

A method as recited in Example 19, wherein the microorganism type isdefined as a particular strain of virus and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular viral strain to enable identification ofthe presence or absence of the particular viral strain in the sample.

Example 25

A method as recited in Example 19, wherein the microorganism type isdefined as a particular strain of virus and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular viral strain to enable identification ofthe presence or absence of the particular viral strain in the sample.

Example 26

A method for identifying the presence or absence of a protein orpolypeptide analyte compound within a sample comprising a mixture ofcompounds that includes a plurality of protein compounds or a pluralityof polypeptide compounds or pluralities of both protein and polypeptidecompounds, the method comprising:

-   -   (a) introducing a portion of the liquid sample into an        electrospray ionization source of a mass spectrometer;    -   (b) forming positively charged ions of the mixture of compounds        of the portion of the liquid sample by electrospray ionization,        the positively charged ions comprising a plurality of        first-generation ion species;    -   (c) isolating a plurality of subsets of the first-generation ion        species comprising respective mass-to-charge (m/z) ratio ranges,        wherein each m/z ratio range includes an m/z ratio of an ion        species comprising a respective protonation state of the analyte        compound;    -   (d) generating a plurality of first-generation product ion        species from the isolated plurality of subsets of the        first-generation ion species by causing the isolated plurality        of subsets of the first-generation ion species to be reacted,        for a predetermined time duration, with reagent anions that,        upon reaction, extract protons from each ion species that        comprises a respective protonation state of the analyte        compound;    -   (e) generating a mass spectrum of the first-generation product        ion species; and    -   (f) identifying either the presence of the analyte compound        within the sample if the mass spectrum comprises one or more        lines at respective predetermined ink ratios that comprise        respective intensities above a predetermined threshold or the        absence of the analyte compound within the sample otherwise.

Example 27

A method as recited in Example 26, further comprising repeatedlyperforming steps (a) through (f) a plurality of times, wherein eachrepetition of step (a) comprises introducing, into the electrosprayionization source, an eluate from a chromatographic column correspondingto a respective retention time.

Example 28

A method as recited in Example 26, wherein the step (f) furthercomprises determining, if the mass spectrum comprises one or more linesat respective predetermined ink ratios that comprise respectiveintensities above a predetermined threshold, a quantity or concentrationof the analyte compound within the sample based on the one or moreintensities.

Example 29

A method as recited in Example 26, further comprising, after the step(b) of forming positively charged ions and prior to the step (c) ofisolating a plurality of subsets of the first-generation ion species,the steps of:

-   -   (b1) isolating a subset of the first-generation ion species        comprising a randomly-selected mass-to-charge (m/z) ratio range;    -   (b2) generating a plurality of product ion species from the        isolated subsets of the first-generation ion species by causing        the isolated subset of the first-generation ion species to be        reacted with reagent anions that, upon reaction, extract protons        from each ion species that comprises a respective protonation        state of the analyte compound or a respective protonation state        of another protein or polypeptide compound;    -   (b3) generating a mass spectrum of the product ion species; and    -   (b4) automatically determining the m/z ratio ranges to be used        in the subsequent step (c), based on the mass spectrum of the        product ions.

Example 30

A method as recited in Example 28, wherein the step (b4) comprisesautomatically determining, from the mass spectrum, a set of m/z ratioscorresponding to multiply-protonated ion species of the other protein orpolypeptide compound.

Example 31

A method of identifying the presence of absence of a microorganism in asample, comprising:

-   -   making an extract of the sample;    -   repeatedly executing the method recited in Example 26 so as to,        at each execution, identify the presence or absence of a        different respective protein or polypeptide analyte compound        within the sample extract; and    -   identifying the presence of the microorganism within the sample        if the presence of each respective protein or polypeptide        analyte compound within the sample extract or the absence of the        microorganism within the sample otherwise.

CONCLUSIONS

The use of PTR-type of ion-ion reactions as taught in this document hasseveral advantages for analysis of complex mixtures of protein orpolypeptide ions. A first significant advantage is provided by thegreatly improved signal-to-noise ratio as may be readily observed bycomparing FIG. 3 with FIG. 4. Even though some charge is lost (i.e.,complete neutralization) as a result of the PTR process, a significantsignal-to-noise ratio is gained as a result of the reaction ofmultiply-charged proteins with singly charged anions. The rate of such areaction is proportional to the square of the product of the charges.Thus, the originally highly-charged analyte ions are converted intoless-charged PTR product ions whose mass spectral signatures appear atsignificantly greater mass-to-charge ratios. By contrast,low-charge-state chemical background ions are less significantlyaffected by the PTR process during a typical experimental reactionperiod because of the low rates of reaction of such ions. This processessentially removes the mass spectral signatures of the proteins andpolypeptides from the low-mass, low-charge-state chemical background“noise”. For example, as shown in FIG. 4, the background ions arerepresented by the large singly-charged peak that is “left behind” atm/z≈642. It is also believed that adducts or water molecules stilladhered to large proteins are removed as a result of the exothermic heatof reaction (at least 125 kcal/mol) deposited by the PTR reaction. Thetransformation of such ions into simple protonated molecules may furtherenhance signal-to-noise characteristics. Potentially, the number ofprotein identifications obtained via this approach could exceed currentcomplex top-down methods utilizing some form of separation technology.

A second important advantage associated with methods in accordance withthe present teachings is provided by greatly improved charge stateassignment. For example, the inventors have experimentally determinedthat approximately 75% of the charge state assignments for individualcharge states may be correctly assigned by employing methods inaccordance with the present teachings. This improved ability torecognize charge states results from the significantly improvedsignal-to-noise ratio. In turn, this provides more accuratedetermination of the molecular weight of the protein or polypeptide.This comparison applies to the current Patterson-FFT charge statealgorithm that is frequently used for real-time charge statedetermination. Another important advantage associated with methods inaccordance with the present teachings is provided by the ability toperform rapid throughput analyses. When combined with the Fast PartialChromatographic Separation technique applied above, these methods allowfor analyses of samples in a high throughput fashion on a time scale ofone minute or less.

The discussion included in this application is intended to serve as abasic description. Although the invention has been described inaccordance with the various embodiments shown and described, one ofordinary skill in the art will readily recognize that there could bevariations to the embodiments and those variations would be within thespirit and scope of the present invention. Thus, the reader should beaware that the specific discussion may not explicitly describe allembodiments possible; many alternatives are implicit. Accordingly, manymodifications may be made by one of ordinary skill in the art withoutdeparting from the scope of the invention as described by the claims.Neither the description nor the terminology is intended to limit thescope of the invention. Any patents, patent applications, patentapplication publications or other literature mentioned herein are herebyincorporated by reference herein in their respective entirety as iffully set forth herein.

APPENDIX—MATHEMATICAL COMPUTATIONAL METHODS 1. Introduction

Structural elucidation of ionized molecules of complex structure, suchas proteins, is often carried out using a tandem mass spectrometer thatis coupled to a liquid chromatograph. The general technique ofconducting mass spectrometry (MS) analysis of ions generated fromcompounds separated by liquid chromatography (LC) may be referred to as“LC-MS”. If the mass spectrometry analysis is conducted as tandem massspectrometry (MS/MS), then the above-described procedure may be referredto as “LC-MS/MS”. In conventional LC-MS/MS experiments a sample isinitially analyzed by mass spectrometry to determine mass-to-chargeratios (m/z) corresponding to the peaks of interest. The sample is thenanalyzed further by performing product ion MS/MS scans on the selectedpeak(s). Specifically, in a first stage of analysis, frequently referredto as “MS¹”, a full-scan mass spectrum, comprising an initial surveyscan, is obtained. This full-scan spectrum is the followed by theselection (from the results obtained) of one or more precursor ionspecies. The precursor ions of the selected species are subjected toreaction, generally fragmentation such as may be accomplished employinga collision cell or employing another form of fragmentation cell such asthose employing surface-induced dissociation, electron-transferdissociation or photon dissociation. In a second stage, the resultingfragment (product) ions are detected for further analysis (frequentlyreferred to as either “MS/MS” or “MS²”) using either the same or asecond mass analyzer. A resulting product spectrum exhibits a set offragmentation peaks (a fragment set) which, in many instances, may beused as a means to derive structural information relating to theprecursor peptide or protein or other biochemical oligomer. It should benoted that, using the fragment ions as a starting population, theprocess of ion selection and subsequent fragmentation may be repeatedyet again, thereby yielding an “MS³” spectrum. In the general case, amass spectrum obtained after (n−1) iterated stages of selection andfragmentation may be referred to as an “MS^(n)” spectrum. This is atime-consuming process because the sample needs to be analyzed at leasttwice and the MS/MS data is only recorded for a limited number ofcomponents.

Most presently available mass spectrometers capable of tandem analysisare equipped with an automatic data-dependent function whereby, whenselecting the precursor ion for MS² analysis from the ion peaks in MS¹,the ion precursors are selected in decreasing intensities. In a simpledata-dependent experiment shown in FIG. 12A, a detector continuouslymeasures total current attributable to ions entering a mass spectrometerdetector. A threshold intensity level A8 of the total ion current is setbelow which only MS¹ data is acquired. As a first component—detected aspeak A10—elutes, the total ion current intensity crosses the thresholdA8 at time t1. When this occurs, an on-board processor or othercontroller of the mass spectrometer determines the most intense ion inthe MS¹ spectra and immediately initiates an MS/MS scan with regard tothe most intense ion. Subsequently, the leading edge of another elutionpeak A12 is detected. When the total ion current once again breaches thethreshold intensity A8 at time t3, an MS/MS scan is initiated withregard to the most intense ion detected after time t3. Generally, thepeak A12 will correspond to the elution of a different chemicalcomponent and, thus, the most abundant ion detected after time t3 willbe different from the ion for which MS/MS analysis was conducted duringthe elution peak A10. In this way, both MS and MS/MS spectra areacquired on each component as it elutes.

The simple data dependent experiment described above works well withchromatographically resolved or partially resolved components, as areillustrated in FIG. 12A. However, in a very complex mixture there may becomponents whose elution peaks completely overlap, as illustrated in thegraph of ion current intensity versus retention time in FIG. 12B. Inthis example elution peak A11 represents the ion current attributable toion m11, and elution peak A13 represents the ion current attributable toion m13, the masses of these ions being schematically illustrated in themass spectrum representation in inset box A16. In the hypotheticalsituation shown in FIG. 12B, there is almost perfect overlap of theelution of the compounds that give rise to ions m11 and m13, with themass spectral intensity of ion m11 always being greater than that of ionm13 during the course of the elution. Under these conditions, the simpledata-dependent technique discussed above with reference to FIG. 12A willfail to ever initiate MS/MS analysis of ion m13 (and possibly otherimportant ions), since only the most intense component (mu) will beselected for MS/MS.

The hypothetical two-ion situation illustrated in FIG. 12B is asimplified example. Most modern mass spectrometer instruments arecapable of performing a series of MS/MS analyses with regard to eachrespective one of several abundant ions detected in an MS¹ analysis.Typically, instead of choosing just a single most-abundant precursor,modern instruments will select the “top P number of the most abundantprecursors” for tandem mass analysis based on the information of apreceding MS¹ data acquisition, where the number P is either a constantor perhaps a variable input by a user. Nonetheless, the basic issuedemonstrated by FIG. 12B remains, especially for multicomponent samplesof biopolymer analytes which may give rise to tens to hundreds of massspectral peaks in a single mass spectrum. Regardless of how such asample is introduced into a mass spectrometer (for example, bychromatographic separation, flow injection, or capillaryelectrophoresis; as a chemical separate delivered from a lab-on-a-chipdevice, by infusion or other method), more than one analyte may berepresented in a single mass spectrum from a single time point, and eachsuch analyte may give rise to many ions, as illustrated in hypotheticalmass spectrum illustrated in FIG. 12C. In FIG. 12C, solid vertical linesoutlined by envelope A208 represent centroids of a first set of massspectral peaks generated from a first analyte compound and dottedvertical lines outlined by envelope A206 represent centroids of a secondset of mass spectral peaks generated from a second co-eluting analytecompound. It is evident that, even if the number, P, of most-abundantpeaks to be analyzed is equal to 10, for example, than only the ions ofonly one of the analyte compounds will be selected for MS/MS analysisusing the traditional data dependent methods described above.Information relating to the second analyte will be lost. Further, thedata so obtained will comprise redundant information on the samecomponent.

To more successfully address the complexities of mass spectral analysisof co-eluting compounds, many mass spectral instruments also employ theso-called “Dynamic Exclusion” principle by which a mass-to-charge ratiois temporarily put into an exclusion list after its MS^(n) spectrum isacquired. The excluded mass-to-charge ratio is not analyzed by MS^(n)again until a certain time duration has elapsed after the prior MS^(n)spectrum acquisition. This technique minimizes a chance of fragmentingthe same precursor ion in several subsequent scans, and allows a massspectrometer to collect MS^(n) spectra on other components having lessintense peaks which would otherwise not be examined. After a selectedperiod of time the excluded ion will be removed from the list so thatany other compounds with the same mass-to-charge ratio can be analyzed.This time duration during which the ion species is on the exclusion listis generally estimated based on an average or estimated chromatographicpeak width. Thus, use of the Dynamic Exclusion principle allows moredata to be obtained on more components in complex mixtures.

Unfortunately, existing dynamic exclusion techniques may perform poorlyfor analyzing mass spectra of mixtures of complex biomolecules. Forexample, consider once again the hypothetical situation illustrated inFIG. 12C. If the ions depicted in FIG. 12C are analyzed using thedynamic exclusion principle, then at least 10 ion species derived from asingle analyte (outlined by envelope A208) will be analyzed, indecreasing order of their intensities in the illustrated MS¹ spectrum,by MS^(n) analysis prior to any peaks from the less abundant analyte(outlined by envelope A206) being considered. This sequence will occurregardless of the fact that each precursor each ions species is placedonto an exclusion list after its respective analysis. The amount of timeconsumed performing ten unnecessarily redundant MS^(n) analyses may thenlead to expiration of the exclusion time of the most abundant ion (ormay lead to exhaustion of the time available to fully analyze a smallnumber of most abundant ions), after which the entire sequence may ofMS^(n) analyses may be repeated.

A further complicating factor in the application of the dynamicexclusion principle to mass analysis of mixtures of complex biomoleculesderives from the fact that the elution profiles of the various compoundsare highly variable and difficult to predict. Different biopolymercompounds may exhibit different elution profiles as a result of complexinteractions between a chromatographic stationary phase and a biopolymerwith multiple molecular interaction sites. Moreover, the time profilesof various ions generated from even a single such compound may fail tocorrelate with the elution profile of the un-ionized compound or withthe profiles of one another as a result of ionization suppression withinan ionization source of a mass spectrometer.

As an example of the elution profile variability that may beencountered, FIG. 13 illustrates a set of chromatograms collected from asingle liquid chromatography-mass spectrometry experimental run of anE.Coli extract. Total ion current is shown in the topmost chromatogram(curve A40) and various extracted ion chromatograms, illustrating theion current that is contributed by respective m/z-ratio ranges are shownin the lowermost five plots (curves A50, A60, A70, A80 and A90). CurveA50 represents the m/z range 660.0-660.5 Da. Similarly, curves A60, A70,A80 and A90 represent m/z ranges 700.5-701.5 Da, 1114.5-1114.5 Da,942.5-943.5 Da and 540.5-540.5 Da. Peaks A1, A2 and A3 are examples ofpeaks with broad chromatographic profiles. Peaks A4 and A5 are examplesof narrow profiles. Peak A6 shows an extremely broad peak. The peakwidths span over an order of magnitude, therefore severely limiting theapplicability of an exclusion list having a pre-defined exclusion timeduration. To address the above computational difficulties, the followingdescribes an improved optimized computational method for making chargestate assignments and for real-time recognition of multiplexed chargestate distributions, this method referred to as the method of “Top PUnique Analyte-Specific Clusters”.

2. Key Features of Self Consistent Map Charge Assignment Algorithm

2.1. Use of centroids exclusively. Standard mass spectral chargeassignment algorithms (e.g., Senko et al., 1995) use full profile dataof the lines in a mass spectrum. By contrast, the novel approach whichis employed in the present methods uses centroids. The key advantage ofusing centroids over line profiles is data reduction. Typically thenumber of profile data points is about an order of magnitude larger thanthat of the centroids. Any algorithm that uses centroids will gain asignificant advantage in computational efficiency over that standardassignment method. For applications that demand real-time chargeassignment, it is preferable to design an algorithm that only requirescentroid data. The main disadvantage to using centroids is imprecisionof the m/z values. Factors such as mass accuracy, resolution and peakpicking efficiency all tend to compromise the quality of the centroiddata. But these concerns can be mostly mitigated by factoring in the m/zimprecision into the algorithm which employs centroid data.

2.2. Intensity is binary. Another key departure from most existingalgorithms is the encoding of intensities as binary (or Boolean)variables (true/false or present/absent) according to the presentmethods. The present methods only take into consideration whether acentroid intensity is above a threshold or not. If the intensity valuemeets a user-settable criterion based on signal intensity orsignal-to-noise ratio or both, then that intensity value assumes aBoolean “True” value, otherwise a value of “False” is assigned,regardless of the actual numerical value of the intensity. Again theencoding of a numerical value as a simple binary value results in asignificant data reduction. In many programming languages, adouble-precision value uses eight bytes of memory storage whereas abinary (or Boolean) value uses just a single byte. Also, comparingBooleans is intrinsically much faster than comparing double-precisionvariables. A well-known disadvantage of using a Boolean value is theloss of information. However, if one has an abundance of data points towork with—for example, thousands of centroids in a typical highresolution spectrum, the loss of intensity information is more thancompensated for by the sheer number of Boolean variables. Accordingly,the inventors' approach and, consequentially, the algorithms taughtherein, exploit this data abundance to achieve both efficiency andaccuracy.

Nonetheless, additional accuracy without significant computational speedloss can be realized by using approximate intensity values rather thanjust a Boolean true/false variable. For example, one can envision thesituation where only peaks of similar heights are compared to eachother. One can easily accommodate the added information by discretizingthe intensity values into a small number of low-resolution bins (e.g.,“low”, “medium”, “high” and “very high”). Such binning can achieve agood balance of having “height information” without sacrificing thecomputational simplicity of a very simplified representation ofintensities.

2.3. Mass-to-charge values are transformed and assembled intolow-resolution bins and relative charge state intervals are pre-computedonce and cached for efficiency. Another innovation of the approachtaught in the present disclosure is in transformation of m/z values ofmass spectral lines from their normal linear scale in Daltons into amore natural dimensionless logarithmic representation. As may be seenfrom the detailed discussion following, this transformation greatlysimplifies the computation of m/z values for any peaks that belong tothe same protein, for example, but represent potentially differentcharge states. This transformation involves no compromise in precision.When performing calculations with the transformed variables, one cantake advantage of cached relative m/z values to improve thecomputational efficiency.

2.4. Simple counting-based scoring and statistical selection criterion.Combining the encoding of centroid intensities as Boolean values, andthe transformation of m/z values, the present approach encodes the wholecontent of any mass spectrum in question into a single Boolean-valuedarray. The scoring of charge states reduces to just a simple counting ofyes or no (true or false) of the Boolean variables at transformed m/zpositions appropriate to the charge states being queried. Again, thisapproach bypasses computationally expensive operations involvingdouble-precision variables. Once the scores are compiled for a range ofpotential charge states, the optimal value can easily be picked out by asimple statistical procedure. Using a statistical criterion is morerigorous and reliable than using an arbitrary score cutoff or justpicking the highest scoring charge state.

2.5. Iterative process to achieve optimality and defined by completeself consistency of charge assignment. The final key feature of thepresent novel approach is the use of an appropriate optimality conditionthat leads the charge-assignment towards a solution. The optimalcondition is simply defined to be most consistent assignment of chargesof all centroids of the spectra. Underlying this condition is thereasoning that the charge state assigned to each centroid should beconsistent with those assigned to other centroids in the spectrum. Thepresent algorithm implements an iterative procedure to generate thecharge state assignments as guided by the above optimality condition.This procedure conforms to accepted norms of an optimization procedure.That is, an appropriate optimality condition is first defined and thenan algorithm is designed to meet this condition and, finally, one canthen judge the effectiveness of the algorithm by how well it satisfiesthe optimality condition. Most existing approaches lack this logicalframework, and their theoretical merits are therefore difficult toassess objectively.

3. Details of Decomposition Algorithm

The inventors have developed methods that, inter alia, are capable ofassigning self-consistent charge states to mass spectral lines anddecomposing complex mass spectra comprising overlapping informationpertaining to several analytes into multiple sets of lines, wherein eachset of lines corresponds to a respective analyte. FIG. 14 is an overviewflowchart of a general set of steps in accordance with the presentteachings for accomplishing these results. Several operations listed inFIG. 14 are illustrated in greater detail in other flow diagrams of theaccompanying set of drawings.

3.1. High-level methods. As shown, FIG. 14 depicts at least two generalexecution or workflow pathways. According to a first general executionpathway or workflow—here termed “File-Deconvolution Workflow” only forpurposes of reference—the methods of the present teachings are employedfor the purposes of analyzing and possibly interpreting previouslycollected and stored mass spectral data. According to a second generalexecution pathway or workflow—here termed “Data-Dependent-AcquisitionWorkflow” only for purposes of reference—the methods of the presentteachings are employed in a “real-time” or “online” fashion at the timethat mass spectral data is being acquired and at least some aspects ofthe course of data acquisition are determined or controlled based on theresults of computations or algorithms in accordance with the invention.Some steps illustrated in FIG. 14 are common to both of theabove-defined execution pathways and are denoted in FIG. 14 by boxesdefined by double lines. Other steps are exclusive to theData-Dependent-Acquisition Workflow pathway and are denoted by boxesdefined by dashed lines. At least one step—step A312—is exclusive to theFile-Deconvolution Workflow pathway and is denoted by a box defined by adotted line. Finally, steps A920 and A925, which are depicted by boxeswith single solid lines, are optional with regard to theData-Dependent-Acquisition Workflow but will generally be performed inconjunction with the File-Deconvolution Workflow. The File-DeconvolutionWorkflow will typically follow the general pathway indicated by dottedarrows at the lower portion of FIG. 14.

Still with reference to FIG. 14, the File-Deconvolution Workflowcommences at step A312, in which previously acquired and stored massspectral data in the form of at least one mass spectrum is input from anelectronic storage device and made available for use in subsequentanalysis. The mass spectrum may be an MS¹ spectrum, an MS² spectrum or,generally, any form of MS^(n) spectrum. By contrast, theData-Dependent-Acquisition Workflow begins at step A310 in which asample is introduced into a mass spectrometer and is subsequentlyionized in step A315. The sample introduction may be from achromatograph, by means of injection or by other means. An MS¹ spectrumof the ions is generated in step A320. It is assumed that steps similarto steps A310, A315 and A320 would have been performed in the generationof the data that is input in the alternative pathway that includes stepA312.

In step A325, new peak centroids (i.e., centroids not previouslyidentified during the experiment in question or in a prior MS¹ spectrumof the input data); are identified and added to a list of centroids. Inthe next step A400, the m/z values of the centroids are transformed andthe intensity data is converted to a Boolean-valued data array in whichbins are assigned over the transformed m/z scale. The step A400comprises a first substep A420 of constructing and populating a Booleanoccupancy array and a second substep A460 of constructing and populatinga relative separation matrix (see FIG. 15). The details of thesesubsteps are described in greater detail in a subsequent section of thisdisclosure.

In step A510, which only applies to the Data-Dependent-AcquisitionWorkflow, centroids of analytes for which MS^(n) analysis has beencompleted are removed from a “selection list” and may be added to an“exclusion list”, if mass analysis is being performed on a sample whosecomposition is time varying, such as upon an effluent from achromatographic column. The selection list includes one or moremass-to-charge (m/z) values or value ranges which are to be analyzed orwhich are being analyzed by the mass spectrometer by tandem massanalysis (MS/MS analysis) or possibly by MS^(n) analysis, each such m/zvalue or range corresponding to a chemical component of the sample asidentified by the methods of the present teachings. The exclusion listincludes one or more mass-to-charge (m/z) values or value ranges whichare to be excluded from future analysis either for the duration of anexperiment or for a temporary time period during the experiment. Thetemporary time period, if employed, may be determined according tomethods of the present teachings, as described in a subsequent portionof this disclosure. Alternatively for direct infusion or flow injectionanalysis, the one or more mass-to-charge values or value ranges whichare to be excluded from future analysis can be performed on signal rankbasis. Centroids depicting low-intensity mass spectral lines are removedfrom the exclusion and selection lists in step A515. The removed m/zvalues or ranges may be later added to the selection list if thecorresponding mass spectral signal intensities subsequently increaseduring an experimental run.

In step A600 tentative charge states assignments are made as outlined inFIG. 17 and further discussed below with reference to that figure. Then,in step A700, the tentatively assigned charge states are adjusted andfinal charge state assignments are made using requirements forself-consistency. The details of this process are outlined in FIG. 18and further discussed below with reference to that figure. Once thefinal charge state assignments have been made, the experimentallyobserved centroids are decomposed into analyte-specific clusters in stepA800 using information derived from the spacing of isotopic clusters.The details of step A800 are illustrated in FIG. 19 and describedfurther with reference to that figure.

The execution of the method A300 may branch at step A910 along one oftwo possible execution paths indicated by solid-line arrows anddotted-line arrows, respectively. If real-time tandem mass spectrometryis being controlled by the results of the prior data analysis, then themethod execution may follow the “N” branch (denoted by solid lines) fromstep A910 directly to step A915, thereby skipping steps A920 and A925.Alternatively, if more data analysis operations are to be conducted uponMS¹ data measured in step A320 or if data was previously input in stepA312, then the “Y” branch of step A910 is followed whereafter molecularweights may be calculated or analyte species identified (step A920) andthe results of the calculations may be reported or stored (step A925).As determined at step A915, if tandem mass spectrometry is to beperformed, as will generally be true if the Data-Dependent-AcquisitionWorkflow execution path is being followed, then the method branchesalong the “Y” branch to step A930. Otherwise, execution proceeds, alongthe “N” branch to step A960.

Considering, now, the “online” execution path illustrated on theright-hand side of FIG. 14, a determination is made in step A930 ifcentroids attributable to known adducts are present in the consideredset of centroids. Is so (the “Y” branch of step A930) then the centroidscorresponding to adduct species or to otherwise-modified species (forinstance, species generated from loss of a neutral molecule) are addedto the exclusion list in step A935. Otherwise, step A935 is bypassed.Step A940 is the commencement of top-down analysis in which arepresentative peak is selected for fragmentation from each of top Panalyte-specific clusters determined in step A800. The following stepsA945, A950 and A955 are conventional steps of, respectively, isolatingions of the m/z ratios corresponding to the selected centroids,fragmenting the isolated ions and performing a mass analysis (MS²) ofthe product ions.

Execution of the method A300 may end after step A960, if either the massspectral experimentation or the data analysis is complete. Otherwise,execution passes back to either step A310 at which the next portion ofsample is introduced to the mass spectrometer or to step A312 at whichthe next portion of mass spectral data is input.

3.2. Building a Boolean-valued occupancy array. FIG. 16 shows thedetails of the step A420 of building an occupancy array, [O_(k)]. Thevalues of the array are Boolean variables and the indices of the arraycorrespond to the discretized transformed mass/charge values. The stepA420 takes, as input, a collection of centroids, C_(i) (1≤i≤L) where Lis an observed number of mass spectral lines. Each C_(i) ischaracterized by its mass/charge (m/z)_(i), its intensity I_(i), itssignal-to-noise ratio (S/N)_(i) and its resolution R_(i). Next, afiltering of the centroids is performed (step A422) by collecting thesubset {

} of centroids which pass a user settable criterion of intensity andsignal to noise thresholds. Next, in step A424, a mass/chargetransformation is performed on each C_(i) in {

} by taking the natural log of the mass/charge value minus that of themass of a proton, M_(proton) as in Eq. 1.

T(m/z)_(i)=In((m/z)_(i) −M _(proton))   Eq. (1)

After this transformation, each centroid, C_(i) in the subset {

} is characterized by T(m/z)_(i), I_(i), (S/N)_(i) and R_(i). Thegreatest, T(m/z)_(High), and the smallest, T(m/z)_(low), values of theT(m/z) values from subset {

} are noted in step A426. This information is then used to create thearray [O_(k)] of values, where each element of the array is aBoolean-valued “occupancy” which maintains a record of whether or not a“signal” is deemed to occur at the respective transformed mass-to-chargevalue, T(m/z)_(k), associated with the array element. Upon creation,each element, O_(k), of the array is initialized to the Boolean value“FALSE”. The number of discrete elements in the array, or “length” ofthe array [O_(k)] is denoted as L_(occs), which is determined as

$\begin{matrix}{L_{occs} = \frac{\left( {{T\left( {m\text{/}z} \right)}_{high} - {T\left( {m\text{/}z} \right)}_{low}} \right)}{D}} & {{Eq}.\mspace{14mu} (2)}\end{matrix}$

where D is the width of each bin in the array and is D=MA/10⁶, where MA,typically 10, denotes a user settable parameter of the mass accuracy ofthe spectrum of interest.

After creation and initialization, the array [O_(k)] must be populated(performed in step A436) with meaningful values. The elements of theoccupancy array [O_(k)] are indexed by the variable, k(1≤k≤L_(occs))whereas the elements of the filtered centroid subset {

} are indexed by the variable, i. The latter indices are converted intocorresponding k-values in step A430, in which, for each centroid, C_(i),in the subset {

}, the corresponding index, k_(i), is determined as follows:

$\begin{matrix}{k_{i} = \frac{\left( {{T\left( {m\text{/}z} \right)}_{i} - {T\left( {m\text{/}z} \right)}_{low}} \right)}{D}} & {{Eq}.\mspace{14mu} (3)}\end{matrix}$

and is rounded to the nearest integer (the rounding operation isindicated by the operator “ROUND[ ]” in FIG. 16. If the resolution,R_(i), of the centroid C_(i) is available (some spectra such as thosecollected in the centroid mode, may not have this defined), then the “Y”branch of the decision step A432 is followed, in which the additionalindices k_(i) ^(Lo) and k_(i) ^(Hi) are calculated in step A434 a asfollows

$\begin{matrix}{k_{i}^{Lo} = \frac{\left( {{T\left( {m\text{/}z} \right)}_{i} - {0.5\left( R_{i} \right)}} \right)}{D}} & {{Eq}.\mspace{14mu} \left( {4a} \right)}\end{matrix}$

$\begin{matrix}{k_{i}^{Hi} = \frac{\left( {{T\left( {m\text{/}z} \right)}_{i} - {0.5\left( R_{i} \right)}} \right)}{D}} & {{Eq}.\mspace{14mu} \left( {4b} \right)}\end{matrix}$

with values rounded to the nearest integer. In cases in which R_(i) isnot available, these indices are instead set to k_(i)−1 and k_(i)+1,respectively, in step A434 b. Finally, in step A436, array values areall set to the Boolean value “TRUE” for indices ranging from k_(i) ^(Lo)to k_(i) ^(Hi), namely

O _(k):=TRUE; k _(i) ^(Lo) ≤k≤k _(i) ^(Hi)   Eq. (5)

3.3. Building a relative separation matrix (RSM). As shown in FIG. 15,step A460 is the step of constructing a relative separation matrix andis the second sub-step of the general step A400. The creation of arelative separation matrix is motivated by observation that, given twocentroids C₁ and C₂, then, if they belong to the same protein isotopicpeak but differ just in charge states, then their mass/charge values arerelated as

|z ₁|×((m/z)₁ −M _(proton))=|z ₂|×((m/z)₂ −M _(proton))   Eq. (6)

in which z₁ and z₂ are the charge state of the centroids C₁ and C₂respectively, and M_(proton) is the mass of a proton. The charge statevalues, z₁ and z₂, will generally be either all positive or all negativedepending on the mode of ionization used in the mass spectrometerinstrument conducting the analyses. Performing the transformation asdescribed in Eq. (1) yields the relationship that

T(m/z)₁ =T(m/z)₂+ln|z ₂ /z ₁|  Eq. (7)

The important property of Eq. (7) is that the transformed T(m/z)_(i)values at different charge states are related by an additive factor thatis independent of the transformed values. Thus one can pre-compute andcache the quantities ln(z₂/z₁) as a matrix that can be reused insubsequent calculations by simple look-ups by pre-computing the RSM. Theabsolute values of the charge states will generally range between unityand some maximum value, |Z_(max)| or, more specifically, 1≤z₁,z₂≤|Z_(max)|. The last step is to discretize the ln|z₂/z₁| matrix bydividing by D as in Eq. (4):

$\begin{matrix}{{RSM}_{{z\; 1},{z\; 2}} = \frac{\left. \ln \mspace{14mu} \middle| {z_{2}\text{/}z_{1}} \right|}{D}} & {{Eq}.\mspace{14mu} (8)}\end{matrix}$

The limits of the matrix, determined by Z_(max), may be set by a useranticipating the maximum and minimum charge states that will beencountered in a set of spectra. Alternatively, Z_(max) may be apre-determined or pre-calculated value. Typically, the absolute valuesof the charge states range from 1 to 50 for a top down experiment. So insuch a case, RSM will be a 50×50 anti-symmetric matrix.

3.4. Building a scoring distribution for each centroid and using it toassign tentative charge states. Before a self-consistent set of chargeassignments may be determined by iteration (in step A700, FIG. 18), areasonable initial set of tentative charge assignments must beformulated. The step A600, the details of which are shown in FIGS. 17Aand 17B, generates this initial set of by assigning a likely chargestate to various of the centroids of subset {

}. Steps A601-A615 consider each such centroid, in turn, and, for eachconsidered centroid, step through various putative values of putativecharge state, z, from a minimum charge state value, Z_(min) up to amaximum charge state value, Z_(max). For example, putative charge statesfrom z=1 through z=50 might be considered for each centroid. For eachcombination of a centroid, C_(i) (as selected in step A601 or step A615)and a putative charge state z₁, (as set in either step A603 of A609), aset of “probe indices” k_(p)(C_(i),z_(i)) is calculated in step A605.The probe indices are a set of k-values that reference bins of theoccupancy array, [O_(k)], for purposes of testing for “TRUE” values ateach of these indices. The k_(p)(C_(i),z_(i)) matrix includes a firstrow having the indices corresponding to the discretized T(m/z)_(i)values of the (+/−m) theoretical isotopic peaks of the selected centroidC_(i). For example, if m=5, the probe indices corresponding to the(+/−5) theoretical isotopic peaks are the transformed values of:

${\left( {m\text{/}z} \right)_{i} - \frac{(5)(1.003)}{z}},{\left( {m\text{/}z} \right)_{i} - \frac{(4)(1.003)}{z}},\ldots,{\left( {m\text{/}z} \right)_{i} + \frac{(5)(1.003)}{z}}$

The k_(p)(C_(i),z_(i)) matrix also includes two additional rows, theelements of which are calculated by generating, for each of the 2m probeindices in the row described above, an additional probe indexcorresponding to expected location of the z−1 peak and anotheradditional probe index corresponding to the expected location of the z+1peaks. Specifically, the indices [k_(p)(C_(i),z_(i))+RSM(z_(i)−1,z_(i))] and [k_(p)(C_(i),z_(i))+RSM(z_(i)+1, z_(i))] are generated,where RSM is the pre-computed and cached relative separation matrixdescribed above. Note that the k_(i) index of the centroid C_(i),itself, is excluded from the probe indices matrix because, at this stageof execution of the algorithm, it is given that the occupancy arraycontains a value of “TRUE” at such index. Similarly, one can alsoincrease the probe matrix in include more charge states of (z−m, z−m+1,. . . , z+m−1, z+m) instead of just (z−1, z, z+1) as described above.

In step A607, a score value is calculated for each tested z value andeach centroid C_(i). The set of scores is used to generate a scoringdistribution for each z value. Each score S(z) is calculated by summing,for each possible value of z₁, the experimentally-derived occupancyvalues. Specifically, the score for each value of z is determined by

S(z)=ΣO _(k) /C   Eq. (9)

where the sum is over k of k_(p)(C_(i),z_(i)) such that (1≤k≤L_(occs))and C is just the number of such k's. In other words, the score at z isjust the fraction of k_(p)(C_(i),z_(i)) indices that are “occupied” by ameasured above-threshold mass spectral signal (i.e., a value of “TRUE”)as coded in occupancy array constructed in step A420 (FIG. 15). Thus,the calculation in step A605 is a form of streamlined approximate “innerproduct” calculation, with the greatest possible score of any singlecalculation being unity. The score distribution is formed by summing thescores for each value of z from the lowest to the highest user settablelimits. Using our example of 1 and 50 as the low and high limits, wewill end up with a distribution of 50 scores for each centroid.

Decision step A611 determines, for each centroid, if the maximum valueof z has been considered. If not then execution returns to step A605 forcalculation of probe indices with a new value of z (as set in stepA609). Otherwise, execution branches to decision step A613 whichdetermines if the last centroid in the subset {

} has been considered. If not, then execution proceeds to step A615 inwhich the next centroid is selected and then to step A603 in which thez-value is reset to its initial state. Otherwise, execution proceeds tostep A617 (FIG. 17B) at which the process of formulating tentativecharge assignments is begun.

Steps A617-A635 shown in FIG. 17B illustrate the process of makingtentative charge assignments using the scoring distributions previouslygenerated in multiple iterations of step A607 (FIG. 17A). In step A617,the first centroid is selected; later the choice of centroid beingconsidered is updated in step A635. After either of these two steps, themean, μ, and standard deviation, σ, of the respective scoringdistribution is computed in step A620. Thus, repeated iteration of stepsA620-A635 causes these statistical measures to be computed for thescoring distribution associated with each centroid. In step A625, ifthere are any scores larger than mean μ+3σ, then the z-value with thelargest score is assigned to the centroid as the initial charge-stateassignment. If there are no scores larger than μ+3σ, then a null valueas provided as the initial assignment for the centroid in question.

3.5. Achieving optimality of completely self consistent chargeassignment by iteration. After the tentative charge-state assignmentshave been made in step A600, execution of the method A300 (FIG. 14)proceeds to step A700 in which the tentative charge state assignmentsare adjusted. Details of the step A700 are shown in FIG. 18. The optimalcondition is simply defined to be most consistent assignment of chargesof all centroids of the spectra. Underlying this condition is thereasoning that the charge state assigned to each centroid should beconsistent with those assigned to other centroids in the spectrum.

The details of the step A700 shown in FIG. 18 implement an iterativeprocedure to generate the charge state assignments as guided by theabove optimality condition. Each centroid with a non-null assignment (asassigned in step A625 of FIG. 17B) is considered, in turn. Each of thesemay be associated with a set of probe indices as indicated in step A605of FIG. 17A. This process is repeated for all centroids with a non-nullassignment, and a new charge state distribution is determined at eachprobe index. Specifically, in step A702, the first or next centroidhaving a non-null tentatively assigned charge state, z_(t), is selected.In step A704, the probe indices for the centroid in question aregenerated, as previously described with respect to step A605 of FIG.17A, if necessary. Then, in step A706, a charge state is calculated ateach of the probe indices corresponding to the centroid in question,assuming that the charge state of the selected centroid is z_(t). Foreach probe index, a record is kept of how many times each charge stateis calculated for that probe index. Before beginning each loop throughsteps A702-A710, these records are cleared (re-set zero) in step A701.Thereafter, during each loop, each time that a charge state iscalculated for a probe index in step A706, the number of times that thecharge state has been so calculated at that probe index is incremented.If, at step A710, there are additional centroids with a non-nullassignment, then execution returns to step A702 and the next suchcentroid is selected.

After the last centroid has been considered, execution branches to stepA712. In step A712, the number of occurrences of each charge state (ascalculated in step A706) are tabulated at each probe index, therebygenerating a charge state distribution for each probe index. Using thenew charge-state distributions, a “charge assignment by majority” (CAM)is obtained in step A714 by adjusting tentative charge state at eachprobe index so at to equal the charge state with the highest number oftabulated at the respective index. The set of all such CAM chargeassignments forms an array of values—the charge assignment by majorityarray.

The charge assignments are considered to be inconsistent if, at stepA716, the values of the CAM array differ from the charge-state valuesused in the generation of the CAM array. By contrast, a completely selfconsistent charge assignment is defined as the assignment of charge ateach index such that it is in complete concordance with that from theCAM array resulting from it. Thus, at step A716, the adjusted tentativecharge states are compared to their prior values. If there has been achange that is greater than a certain tolerable limit, then the chargeassignments are not self-consistent. In this case, the “N” branch ofstep A716 is followed and execution returns to step A701 whereby a newset of calculations are performed so as to achieve self consistency.Thus, a set of repetitions of the CAM array determination are performedby using the charges from each CAM to generate a subsequent CAM.Optimality is achieved when convergence is achieved—that is, the CAMgenerates the same CAM.

In practice, one might not achieve exact convergence by this procedure.However, the inventors' experience shows that, after a few iterations,the incidence of non-concordance becomes negligibly small and thus onecan stop the iteration at a very good charge-state assignment.Accordingly, in step A716, convergence is considered to be operationallyachieved when the difference in successive CAM arrays is within acertain tolerable limit (i.e., within a certain tolerance). In thiscase, execution branches to step A718 at which the final self-consistentcharge state and each centroid is set to be equal to the tentativecharge state at which the operational convergence occurred.

4. Determination of Analyte-Specific Clusters

The clustering approach starts with the clustering criterion defined byEq. (10), in which the number of C¹³ non-monoisotopic peaks, ΔN^(C13),that are reasonably expected to occur within a restricted m/z range isgiven by

$\begin{matrix}{{{Number}\mspace{14mu} {of}\mspace{14mu} C^{13}\mspace{14mu} {Peaks}} = \frac{\left\lbrack {\left( {z_{1}\left( {m\text{/}z} \right)}_{1} \right) - \left( {z_{2}\left( {m\text{/}z} \right)}_{2} \right)} \right\rbrack - {\left( {z_{1} - z_{2}} \right)M_{proton}}}{M_{C\; 13}}} & {{Eq}.\mspace{14mu} (10)}\end{matrix}$

in which z₁ and z₂ are the charge states assigned to mass spectrallines, (m/z)₁ and (m/z)₂ are the experimentally measured mass to chargevalues, M_(C13) is the mass difference between the isotopes of carbon,C¹³ and C¹², and M_(proton) is the mass of a proton. The error (δ) orstandard deviation associated with the calculation is computed from auser-supplied value of accuracy, α, which is defined in ppm, as well asthe resolutions R₁ and R₂ of the centroids under consideration asdescribed in Eq. (11)

$\begin{matrix}{\delta = {\frac{1}{M_{C\; 13}}\sqrt{\begin{matrix}\left\{ {{\left( {\alpha^{2} + {1\text{/}2R_{1}^{2}}} \right)\left( {z_{1}\left( {m\text{/}z} \right)}_{1} \right)^{2}} +} \right. \\\left. {\left( {\alpha^{2} + {1\text{/}2R_{2}^{2}}} \right)\left( {z_{2}\left( {m\text{/}z} \right)}_{2} \right)^{2}} \right\}\end{matrix}}}} & {{Eq}.\mspace{14mu} (11)}\end{matrix}$

To determine if any two centroids (peaks) belong to the sameanalyte-specific cluster (associated with a particular bio-molecule suchas a protein), the theoretical ΔN^(C13) value is calculated using Eq.(10). If the calculated ΔN^(C13) value is an integer within themeasurement error, as computed as in Eq. (11), then the two centroidsare considered to belong to the same analyte-specific cluster, providedthat the number of C¹³ peaks does not exceed a user definedlimit(typically 10 to 15). Of course, one skilled in the art can easilyuse a multitude of other similar statistical tests such as the z-test,or t-test to determine whether the two peaks differ by an integralnumber of C¹³, given the uncertainties of their m/z's as encoded in αand the resolution R's.

The step A800 of decomposing the mass spectral lines intoanalyte-specific clusters shown in FIG. 19 makes use of the abovereasoning. The step A800 considers centroids for which chargeassignments have been made, as previously described. Step A805 beginswith the charge-assigned centroid that has the greatestexperimentally-observed intensity. The so-selected centroid is then usesas a “seed” for the first cluster. Then, proceeding in order ofdecreasing intensity (steps A810 through A830), a check is made todetermine if the next centroid in the list clusters with the seedcentroid of this cluster. This check is performed by first calculatingΔN^(C13) and its error, δ, using Eq. 10 and Eq. 11, respectively (stepA815). If it is noted, in the decision step A820, that thepresently-calculated value of ΔN^(C13) is an integer, within thecalculated error, then execution follows along the “Y” branch to stepA825 in which the centroid under consideration is grouped together withthe seed centroid as belonging to a single cluster. If not, then the “N”branch is followed such that, in step A830, if there are remainingnon-seed centroids, execution returns to step A810 in which thenext-intense non-seed centroid is selected for cluster checking. If, atstep A830, the list of non-seed centroids is exhausted (that is, thereare no remaining non-seed centroids having intensities less than thepresently considered centroid) but there are remaining non-clusteredcentroids (determined in step A835), then execution returns to step A805in which a new cluster is started with using the most-intense non-seedcentroid as the new seed. Subsequent iterations check against allcluster seeds created and create new clusters if the new centroid doesnot cluster with any preceding clusters.

Finally, in step A840, a simple heuristic is employed to determine ifany cluster created by the clustering algorithm is “healthy”. In ourinitial implementation, we use the simple rule that a “healthy” clustermust have at least four distinct charge states or at least N (usersettable, but defaulting to 15) member centroids. We filter out clustersthat are not “healthy” according to these criteria. After the removal of“unhealthy” clusters, the remaining are the final analyte-specificclusters, each representing a different bio-polymer or other high-masscompound.

5. Protein Molecular Weight Calculations

One of the more common ways of calculating the mono-isotopic molecularweight, M_(mono), of a protein from an experimental high-resolutionspectrum is to use the so-called “Averagine” method (Senko, M. W, Beu,S. C. and McLafferty, F. W., 1995, Determination of monoisotopic massesand ion populations for large biomolecules from resolved isotopicdistributions. J. Am. Soc. Mass Spectrom., 6: 229-233), which itself isan extension of an earlier method for low-resolution data (Zubarev, R.A. and Bonddarenko, P. V., 1991, An a-priori relationship between theaverage and monoisotopic masses of peptides and oligonucleotides. RapidCommun. Mass Spectrom., 5: 276-277). Briefly, the Averagine method firstmodels an experimental isotopic cluster by a hypothetical modelmolecule—the “Averagine” molecule. By optimizing the fit between theexperimental and the theoretical isotopic distribution, one can arriveat an estimate of the mono-isotopic mass desired.

The Averagine technique is used within various mass spectrometry peakdecomposition and analysis algorithms that are commercially availablefrom Thermo Fisher Scientific of Waltham Mass. USA. Although theAveragine method has been highly successful, the present inventors aremotivated to develop a different approach based on the followingconsiderations: (1) Calculation speed. Averagine fitting may be timeconsuming, a not insignificant consideration for real-time applications,such as those described herein in which decisions are automaticallymade, in real time, regarding which of several observed ions tofragment. It should be noted, however, that, in situations where a largenumber of spectral fits are not required, calculation speed may not poseany concern; and (2) Mass accuracy. For a larger molecular weightprotein whose signature appears in a crowded spectrum, the correspondingisotopic cluster tends to be noisy and incomplete (missingisotopes—especially the edges, missing charge states etc). The use of anAveragine fit may not be appropriate in such instances.

The present inventors therefore here teach an approach that promises toproduce a robust estimate of the mono-isotopic mass that is very easy tocalculate and more resistant to noise and artifacts. The main goal isrobustness and precision, accepting the compromise that the estimatemight be biased. In short, the estimate might not be the “true”mono-isotopic mass (but nonetheless very close to it), but it should berobust/stable in face of experimental imperfections. The error shoulddeviate from the true mono-isotopic mass by either 0 or +/−1 dalton (1Da) precisely, after taking mass accuracy into consideration. Theinventors here point out that robustness, in many cases, is moreimportant than accuracy. For example, if one were to build a molecularweight database based on experimental data, the ability to produce thesame answer both while building the database and while testing thedatabase by new data is generally desired, even if the estimates arepotentially off by 1 Da from the true molecular weight but nonethelessare identical from experiment to experiment.

The approach starts with three simple observations: (1) the isotopicpatterns for most proteins are due to the C¹²/C¹³ binomial distributionand all the other isotopes are of too low an abundance to warrantconsideration; (2) the mode (i.e., the peak having the greatestintensity) of a binomial distribution is a very robust feature of thebinomial distribution compared to either the average, the standarddeviation, or the exact boundaries of the distribution, and (3) for thebinomial distribution, the mode is located less than 1 Da to the left ofthe average (see Table A1, which is presented in FIGS. 20A-20D). Thismeans that the mode is a very usable replacement for the average, whichitself is more difficult to estimate for more noisy data. For example, adistribution truncated at the edges will give rise to an unreliableaverage estimate while the mode, unless the distribution is highlydistorted, is very stable against such truncations.

The starting point for the calculation is defined by M, the observedmode of an isotopic cluster. Zubarev's approach to calculate the firstapproximation of the monoisotopic mass is then employed where:

M ₁ =M×0.999316   Eq. (12)

The second approximation of the monoisotopic mass is then defined by:

M ₂ =M−n×1.003   Eq. (13)

where n is the smallest integer such that M₂≥M₁. Finally, in thecalculation of the monoisotopic mass, M_(mono), if there is anexperimental peak of the cluster which is within 1 Dalton greater thanM₂ then:

M _(mono) =M ₂+1.003   Eq. (14a)

otherwise,

M_(mono)=M₂   Eq. (14b)

This method of calculating the mono-isotopic mass has been incorporatedin the results illustrated herein. The inventors' results show that thepredictions compare very favorably to those predicted by the Averaginemethod. For large proteins, testing on standard proteins indicates thatthe mono-isotopic mass estimate is stable. In addition, a clustermolecular weight is also calculated for closely related peaks orproteoforms. We term the result of such a calculation as the “ClusterMolecular Weight”. After all the proteoforms have been discovered in abatch, a cluster analysis of all the proteoforms is performed using themore discriminatory error function:

Error=min|w ₁ −w ₂ −N×1.003|  Eq. (15)

over −3≤N≤3. If Error<0.5 (w₁+w₂)×10 ppm , then w₁ and w₂ should beconsidered equivalent. Each proteoform will then be mapped into clustersof equivalent proteoforms represented by a consensus monoisotopic mass.This mass is termed and stored as “consensus MW”.

6. Program Input and Output

FIG. 21A shows the starting page (i.e., a visual display screen capture)of a post-data-acquisition version of a computer program that employsthe data dependent methods described herein. On the left hand side ofthe display illustrated in FIG. 21A, the “Raw File” box serves as theinput line for the mass spectrometry data file to be processed. The“Batch Mode” check box can be enabled, thereby allowing a user toprocess multiple data files, while the “Auto Scan Increment” check boxis used to enable processing of consecutive spectra. Results from thepost-data-acquisition version of the program can be plotted in a displayby the user enabling the “Plot Deconv” check box. The minimum andmaximum spectrum (scan) number to process is set by the “Scan buttons”which directly default to the file length (in scans) or which can be setby the user.

Output can be controlled as seen in the lower left hand side of FIG.21A, by causing results to be output to a peak list and by the userspecifying the output as either MS1 or MS2 type data (in csv fileformat). The mass tolerance (Mass Tol) defaults to 3; however this toocan be set by the user. Output can also be produced in a .puf fileformat for input into the ProSight™ PC protein identification program.Details of the spectral decomposition results (also referred to hereinas “deconvolution” results) can also be stored in a .csv file format forfurther data analysis. The deconvolution summary in the “Results” tablists the data file(s) and scan(s) analyzed to produce the report.Moving down the tab are the total number of centroids detected alongwith the number filtered as part of the program. The percentage of peakssuccessfully receiving charge-state assignments is found in the “Zscape”box along with a comparison to results as calculated by one of theleading existing deconvolution programs currently used by those skilledin the state-of-the-art. The “both assigned” and “concordance” boxesmeasure the agreement between the two programs. Moving to the bottom ofthe “Results” tab, the percentage of cluster assigned and the totalnumber of unique proteins deconvoluted are shown. An expanded view ofthis tab is shown in FIG. 21D.

Two of the tabs located on the right hand side of the display shown inFIG. 21A provide for choosing the assignment and clustering parametersassociated with the deconvolution process. In FIG. 21B, the “AssignmentParameters” tab includes the mass accuracy in parts per million (ppm),the minimum peak intensity threshold, the minimum signal-to-noise ratio(s/n) needed, and the lowest and highest charge state expected for thedeconvolution process. These parameters are further divided into twocolumns one each for MS¹ and MS² analysis.

The “Clustering Parameters” tab shown in FIG. 21C is also divided intotwo columns relating to MS¹ and MS² analysis respectively. Provision ismade for user input of the minimum number of contiguous charge statesand isotopes for the clustering convergence calculation described above.The “Sufficient Contiguous Charge States”, “Sufficient ContiguousIsotopes” and “Mass Separation” parameter input displays are alsopresent on this input tab.

7. Examples

FIG. 22A shows the deconvolution result from a five component proteinmixture consisting of cytochrome c, lysozyme, myoglobin, trypsininhibitor, and carbonic anhydrase. A top display panel A1203 of thedisplay shows the acquired data from the mass spectrometry representedas centroids. A centrally located main display panel A1201 illustrateseach peak as a respective symbol. The horizontally disposedmass-to-charge (m/z) scale A1207 for both the top panel A1203 andcentral panel A1201 is shown below the central panel. The computerdisplay may also include (not specifically shown in FIG. 22A) thesettings for mass accuracy (expressed in ppm), the peaks/isotope clustersetting, the minimum intensity threshold and signal-to-noise settings,and the minimum and maximum charge states associated with thecalculation. The panel A1205 on the left hand side of the display showsthe calculated molecular weight(s), in daltons, of protein molecules.The molecular weight (MW) scale of the side panel A1205 is orientedvertically on the display, which is perpendicular to the horizontallyoriented m/z scale A1207 that pertains to detected ions. Each horizontalline in the central panel A1201 indicates the detection of a protein inthis example with the dotted contour lines corresponding to the ioniccharge states, which are displayed as a direct result of thetransformation calculation discussed previously. In FIG. 22B is shown adisplay pertaining to the same data set in which the molecular weight(MW) scale is greatly expanded with respect to the view shown in FIG.22A. The expanded view of FIG. 22B illustrates well-resolved isotopesfor a single protein charge state (lowermost portion of left hand panelA1205) as well as potential adduct or impurity peaks (two present in thedisplay). The most intense of these three molecules is that of trypsininhibitor protein. A further-expanded view in FIG. 22C shows the exactdetail of the trypsin inhibitor protein at the isotopic level. Thesymbol size used to represent the individual isotopes is scaledaccording to the intensity of each isotope peak.

FIG. 23A shows the data and deconvolution results of a crude extractfrom the bacterium E. coli. This sample was directly infused into themass spectrometer using only a single stage of mass spectrometry. Thecalculated results, obtained using methods in accordance with thepresent teachings, indicate the presence of 58 unique discernableproteins in this sample. Many of the proteins in this example haveoverlapping charge states which are easily clustered using theaforementioned algorithm. FIG. 23B illustrates another displaycorresponding to the same data set showing an expanded view of the m/zscale in the vicinity of m/z=700 Da/e (as well as an expanded view ofthe MW scale in Daltons) showing three distinct charge states depictedby differently patterned centroids in the top panel A1203. The centroidsA1301 in the top panel A1203 of the display correspond to a +22isotopically resolved charge state of a protein of mass 15,305.76 Da. Inthis case, this is the only charge state distribution present, yet thealgorithm correctly identifies the cluster even though the centroid barsA1303 and A1305 occur within 1 Da of the charge state in question. Manycurrently available deconvolution programs cannot correctly assigncharge state to independent distributions (two different proteins)within a 3 Da window. Also, the centroid bars A1305 represent the +23charge state of a protein from E. coli of mass 16,017.57 Da. Note thatthe +23 charge state of this protein directly overlaps with the centroidbars A1303 of a separate +22 charge state protein of mass 15327.47 Da.Typical deconvolution programs are unable to correctly assign peaks inspectra having this kind of closely spaced or overlapping charge statesas can be seen by comparison to FIG. 13C, which shows the same massspectrum acquired and processed using a program employing a conventionalalgorithm. The conventional approach is unable to make any charge stateassignments in this region of the spectrum, as is indicated by the“question marks” over the peaks of interest in the figure. FIG. 13D hasthe correctly labeled charge states of the original profile data asassigned by our algorithm employing the novel methods taught herein forthe two overlapping charge states described above.

The program employing methods in accordance with the present teachingscan also determine charge states for those peaks that do not containindividually resolved isotopes. In another example, illustrated in FIG.24A, the mass spectrum of an intact antibody is shown with varyingdegrees of glycosylation. An example of the different glycoforms of theantibody are displayed in the inset of FIG. 24A. FIG. 24B illustratesthe deconvoluted molecular weights of the four deconvoluted glycoformsranging from 148378 Da to 148763 Da.

The methods in accordance with the present teachings also have utilityfor deconvoluting tandem mass spectrometry data. In another example, asillustrated in FIGS. 25A and 25B, two charge states from the proteincarbonic anhydrase II were selected for collisional activateddissociation. In FIG. 25A is shown the MS/MS spectrum and correspondingdeconvolution of the +26 charge state of carbonic anhydrase II. Here 64%of the centroids were correctly identified compared to only 9% using theconventional algorithm. Exactly 50% of the centroids were clustered evenin the event where many MS/MS fragments do not produce multiple chargestates of the same fragment. The total number of fragment ionsidentified correctly was 35. FIG. 25B shows the MS/MS fragmentation anddeconvolution of the +21 charge state of carbonic anhydrase II at m/z1001. Here 74% of the centroids were clustered and 78% of the chargestates were assigned correctly. A total of 49 fragments ions wereidentified using the program.

8. Directing Data Dependent Acquisition to Avoid Redundant Measurements

In the traditional approach to setting up a dynamic exclusion list, m/zvalues are placed on the list for a specified time period, whichapproximates the average peak width of a given compound/type ofcompound. When using such an approach with small molecules or peptides(i.e. tryptic peptides which typically have the same physiochemicalproperties), it works well to increase the dynamic range associated withthe compound identification process. On the contrary, intact proteins(as are measured in top-down proteomics studies) widely vary in sizes,amino acid compositions, physiochemical properties, and 3-D structures.This variability typically leads to many more sites on the protein (thanwould be the case for smaller-molecule analytes) interacting with thestationary phase of a chromatographic column. The result is that somepeaks may be only a few seconds wide while others can persist on theorder of minutes. A typical example of the variability that can beexpected is illustrated in FIG. 13, showing the varying peak profilesobtained from a single chromatographic run. Therefore, the standardapproach to dynamic exclusion is not an ideal fit for top-down analysis.To rectify this problem, the present methods employ a signal intensityranking system to determine for how long the charge states associatedwith a given protein should be placed on the dynamic exclusion list. Inthis new approach, the seed centroid of each cluster is put on theexclusion list. When a new seed centroid is proposed in subsequent MS¹scan, a check is first made to determine if the new centroid clusterswith any of the seed centroids presently on the selection list in stepA510 (FIG. 14). If so, a check is made to determine if the intensity ofthe new centroid has fallen below a threshold (as a fraction of theintensity of the original seed centroid). Only when the intensity doesfall below the threshold, will the original seed centroid be taken offof the exclusion list (step A515).

Alternatively, all charge states from a given protein can be placed onthe exclusion list, thus eliminating selecting different charge statesfrom the same protein for tandem MS analysis. While these charge statesare on the dynamic exclusion list, the signal intensity of the peakscomprising the list are monitored until they are below a defined minimumintensity or there is an increase in signal from one of the chargestates at a defined mass difference (ppm), indicating the presence oftwo components of differing mass and charge but the same m/z value.

What is claimed is:
 1. A method of identifying the presence or absenceof a microorganism type in a sample, comprising: (a) identifying a listof analyte compounds whose simultaneous presence in the sample isdiagnostic of the presence of the microorganism type in the sample, saidlist of analyte compounds comprising protein compounds, polypeptidecompounds or both protein and polypeptide compounds; (b) extracting,from the sample, a liquid solution comprising a mixture ofsample-derived proteins and polypeptides; (c) introducing at least afirst portion of the liquid solution into an ionization source of a massspectrometer; (d) generating, from the at least first portion of theliquid solution at the ionization source, positively charged ions of themixture of compounds, the positively charged ions comprising a pluralityof ion species; (e) isolating at least a first subset of the pluralityof ion species, each isolated subset of the at least a first isolatedsubset comprising a respective mass-to-charge (m/z) ratio range; (f)generating a plurality of first-generation product ion species from eachisolated subset of ion species by causing each said isolated subset ofion species to be reacted, for a predetermined time duration, withreagent anions that, upon reaction, extract protons from each of one ormore ion species of said isolated subset of ion species that comprises aprotonated molecular species of a protein or polypeptide compound; (g)generating at least one mass spectrum, using a mass analyzer of the massspectrometer, of either first-generation product ion species orsecond-generation product ion species generated by further reaction ofthe first-generation product ion species; (h) for each respectiveanalyte compound in the list, performing the steps of: (h1) conducting asearch of the at least one mass spectrum of either the first-generationor the second-generation product ion species for a set of one or morem/z ratios that are diagnostic of the respective analyte compound; and(h2) identifying the presence of the respective analyte compound withinthe liquid solution if the set of one or more m/z ratios is identifiedin the mass spectrum; and (i) identifying the presence of themicroorganism type within the sample if the presence of each and everyanalyte compound of the list of analyte compounds is identified withinthe liquid solution.
 2. A method as recited in claim 1, wherein aperforming of the steps (h1) and (h2) is performed concurrently with theperforming of one or more of the steps (c) through (g).
 3. A method asrecited in claim 1, wherein the microorganism type is defined as aparticular genus of bacteria and the list of analyte compounds includesa sufficient number of analyte compounds that are diagnostic of theparticular genus of bacteria to enable identification of the presence orabsence of the particular genus of bacteria in the sample.
 4. A methodas recited in claim 1, wherein the microorganism type is defined as aparticular species of bacteria and the list of analyte compoundsincludes a sufficient number of analyte compounds that are diagnostic ofthe particular species of bacteria to enable identification of thepresence or absence of the particular species of bacteria in the sample.5. A method as recited in claim 1, wherein the microorganism type isdefined as a particular sub-species of bacteria and the list of analytecompounds includes a sufficient number of analyte compounds that arediagnostic of the particular sub-species of bacteria to enableidentification of the presence or absence of the particular sub-speciesof bacteria in the sample.
 6. A method as recited in claim 1, whereinthe microorganism type is defined as a particular strain of virus andthe list of analyte compounds includes a sufficient number of analytecompounds that are diagnostic of the particular viral strain to enableidentification of the presence or absence of the particular viral strainin the sample.
 7. A method as recited in claim 1, wherein themicroorganism type is defined as a particular strain of virus and thelist of analyte compounds includes a sufficient number of analytecompounds that are diagnostic of the particular viral strain to enableidentification of the presence or absence of the particular viral strainin the sample.
 8. A method for identifying the presence or absence of aprotein or polypeptide analyte compound within a sample comprising amixture of compounds that includes a plurality of protein compounds or aplurality of polypeptide compounds or pluralities of both protein andpolypeptide compounds, the method comprising: (a) introducing a portionof the liquid sample into an electrospray ionization source of a massspectrometer; (b) forming positively charged ions of the mixture ofcompounds of the portion of the liquid sample by electrosprayionization, the positively charged ions comprising a plurality offirst-generation ion species; (c) isolating a plurality of subsets ofthe first-generation ion species comprising respective mass-to-charge(m/z) ratio ranges, wherein each m/z ratio range includes an m/z ratioof an ion species comprising a respective protonation state of theanalyte compound; (d) generating a plurality of first-generation production species from the isolated plurality of subsets of thefirst-generation ion species by causing the isolated plurality ofsubsets of the first-generation ion species to be reacted, for apredetermined time duration, with reagent anions that, upon reaction,extract protons from each ion species that comprises a respectiveprotonation state of the analyte compound; (e) generating a massspectrum of the first-generation product ion species; and (f)identifying either the presence of the analyte compound within thesample if the mass spectrum comprises one or more lines at respectivepredetermined ink ratios that comprise respective intensities above apredetermined threshold or the absence of the analyte compound withinthe sample otherwise.
 9. A method as recited in claim 8, furthercomprising repeatedly performing steps (a) through (f) a plurality oftimes, wherein each repetition of step (a) comprises introducing, intothe electrospray ionization source, an eluate from a chromatographiccolumn corresponding to a respective retention time.
 10. A method asrecited in claim 8, wherein the step (f) further comprises determining,if the mass spectrum comprises one or more lines at respectivepredetermined ink ratios that comprise respective intensities above apredetermined threshold, a quantity or concentration of the analytecompound within the sample based on the one or more intensities.
 11. Amethod as recited in claim 8, further comprising, after the step (b) offorming positively charged ions and prior to the step (c) of isolating aplurality of subsets of the first-generation ion species, the steps of:(b1) isolating a subset of the first-generation ion species comprising arandomly-selected mass-to-charge (m/z) ratio range; (b2) generating aplurality of product ion species from the isolated subsets of thefirst-generation ion species by causing the isolated subset of thefirst-generation ion species to be reacted with reagent anions that,upon reaction, extract protons from each ion species that comprises arespective protonation state of the analyte compound or a respectiveprotonation state of another protein or polypeptide compound; (b3)generating a mass spectrum of the product ion species; and (b4)automatically determining the m/z ratio ranges to be used in thesubsequent step (c), based on the mass spectrum of the product ions. 12.A method as recited in claim 11, wherein the step (b4) comprisesautomatically determining, from the mass spectrum, a set of m/z ratioscorresponding to multiply-protonated ion species of the other protein orpolypeptide compound.
 13. A method of identifying the presence orabsence of a microorganism type in a sample, comprising: (a) identifyinga list of analyte compounds whose simultaneous presence in the sample isdiagnostic of the presence of the microorganism type in the sample, saidlist of analyte compounds comprising protein compounds, polypeptidecompounds or both protein and polypeptide compounds; (b) extracting,from the sample, a liquid solution comprising a mixture ofsample-derived proteins and polypeptides; (c) for each respectiveanalyte compound in the list, performing the steps of: (c1) introducinga portion of the liquid solution into an electrospray ionization sourceof a mass spectrometer; (c2) forming positively charged ions of themixture of compounds of the portion of the liquid solution byelectrospray ionization, the positively charged ions comprising aplurality of ion species; (c3) isolating a first subset of the ionspecies comprising a first mass-to-charge (m/z) ratio range thatincludes an m/z ratio of a particular predetermined multiply-protonatedmolecular species of the respective analyte compound; (c4) generating aplurality of first-generation product ion species from the isolatedfirst subset of ion species by causing the isolated first subset of ionspecies to be reacted, for a predetermined time duration, with reagentanions that, upon reaction, extract protons from each of one or more ionspecies that comprises a protonated molecular species of a protein orpolypeptide compound; (c5) generating a mass spectrum, using a massanalyzer, of either the first-generation product ion species or ofsecond-generation product ion species generated from thefirst-generation product ion species; (c6) conducting a search of themass spectrum of either the first-generation or the second-generationproduct ion species for a set of one or more m/z ratios that arediagnostic of the respective analyte compound; and (c7) identifying thepresence of the respective analyte compound within the liquid solutionif the set of one or more m/z ratios is identified in the mass spectrum;and (d) identifying the presence of the microorganism type within thesample if the presence of each and every analyte compound of the list ofanalyte compounds is identified within the liquid solution.